Comparison of Google A2A and IBM ACP Agent Communication Protocols

Google A2A vs. IBM ACP – A Comparison of Agent Communication Protocols

Introduction

In April 2025, two major players introduced new agent communication protocols aimed at standardizing how autonomous AI agents interact: Google’s Agent2Agent (A2A) protocol and IBM’s Agent Communication Protocol (ACP). Both emerged to address the growing need for multi-agent interoperability as organizations deploy multiple AI agents to collaborate on complex tasks. While Google A2A and IBM ACP share the high-level vision of connecting AI agents across systems, they differ in scope, design, and target use cases. This report provides a detailed comparison of Google A2A and IBM ACP, covering their purpose and goals, technical architecture and design principles, communication mechanisms, integration options, security models, scalability considerations, industry positioning, and any early feedback or analysis. Each protocol is described in turn, followed by a comparative discussion of similarities and differences.

Google Agent2Agent (A2A) Protocol

Sources:

• A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb
• Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog.
• Google Open-Sources Agent2Agent Protocol for Agentic Collaboration - InfoQ.
• MCP, ACP, A2A, Oh my! — WorkOS
• Introducing multiagent BeeAI - IBM Research
• The Rise of AI Agent Protocol Wars | by Vikrambalauae Aj - Medium

Purpose and Goals

Google’s A2A is an open protocol for agent-to-agent communication introduced to enable AI agents (from potentially different vendors or frameworks) to seamlessly work together. The goal is to increase agent autonomy and collaborative potential by breaking down silos between agents. In Google’s words, A2A allows agents to “communicate with each other, securely exchange information, and coordinate actions” across various platforms y standardizing agent interaction, Google aims to unlock more complex multi-agent workflows in enterprises – for example, coordinating tasks like IT automation, customer support, or supply chain planning across specialized agents. A2A is positioned as a complement to Anthropic’s Model Context Protocol (MCP) (which standardizes how LLMs connect to tools and data), focusing instead on direct agent-to-agent coordination .
The high-level vision is an interoperability standard so that any agent built with A2A can interoperate with any other A2A-compliant agent, regardless of vendor or framework . Google gathered support from 50+ technology partners (e.g. Atlassian, Salesforce, Box, LangChain, etc.) and consulting firms to back A2A, indicating an industry-wide effort to make it a ubiquitous standard Ultimately, the purpose of A2A is to enable dynamic multi-agent ecosystems in which agents can collaborate to automate complex tasks with greater efficiency and autonomy.

Technical Architecture and Design Principles

Google A2A’s architecture is built on a client–server model between agents: one agent acts as a client that formulates a task request, and another agent acts as a remote server that fulfills the task. This allows an agent to call upon the capabilities of another as if it were an external service. Several key design principles guided A2A’s development:

• Embrace agentic capabilities: The protocol lets agents collaborate in an unstructured, autonomous manner (not simply treating one agent as a “tool” of another). It supports agents that may not share memory or context, truly enabling independent agents to coordinate
• Build on existing standards: A2A is built atop familiar web standards like HTTP for transport, JSON-RPC for messaging, and Server-Sent Events (SSE) for streaming updates. By leveraging popular standards, A2A can integrate more easily with existing systems and developer tools.
• Secure by default: A2A includes enterprise-grade authentication and authorization, aligning with OpenAPI auth schemes (API keys, OAuth2, etc.) from the start. This principle ensures agent interactions can be restricted and trusted in business environments.
• Support long-running tasks: The protocol is designed to handle both quick requests and long-running tasks (minutes, hours, or even days), including scenarios where a human might be in the loop. It provides for real-time feedback, progress updates, and state synchronization over the lifespan of a task.
• Modality agnostic: A2A is not limited to text-based exchanges. It supports multiple content modalities (text, audio, video, etc.), allowing agents to exchange rich data (e.g. streaming audio or generated images) as part of their collaboration.

A2A’s architecture can be visualized as a network of agents where each agent may expose an HTTP endpoint (an A2A server) and can also act as a client to other agents. Agents advertise themselves via an Agent Card (a JSON “manifest”) typically hosted at a well-known URL (e.g. .well-known/agent.json), which describes the agent’s identity, capabilities/skills, version, supported modalities, and required auth for access . This allows discovery of what an agent can do and how to communicate with it. When Agent A wants to delegate a task to Agent B, it will look up B’s Agent Card to see if B has the needed capability and how to call it.

Internally, Google leveraged its experience deploying large-scale agent systems to shape A2A’s design for scalability and robustness, so that many agents can coordinate without ad-hoc integrations. By basing on HTTP/JSON, any programming language or framework can implement the protocol, and many partners are actively contributing to the open-source spec. This open development approach means A2A’s design may evolve with community input, but the core principles ensure the protocol remains vendor-neutral and enterprise-ready.

Communication Mechanisms, Message Format, and Agent Interaction

Caption: A2A client-server interaction. A client agent (blue) issues tasks to a remote agent (green). The A2A protocol defines capabilities like secure collaboration, task/state management, user experience negotiation, and capability discovery for rich multi-agent communication.*

Communication in A2A revolves around the concept of a Task. A client agent creates a task (essentially a request for some work or information) and sends it to a remote agent over HTTP using a JSON-based request. A2A adopts JSON-RPC (a lightweight RPC format using JSON) layered on HTTP, meaning requests and responses are structured in JSON with method names, parameters, and so on. The remote agent receives the task, processes it (possibly using an internal LLM or tools), and returns results or updates. The result of a completed task is called an Artifact – this could be an answer, a file, an image, etc., produced by the agent. If a task cannot be completed immediately, the task enters a lifecycle with states (e.g. submitted, in progress, waiting for input, completed). Throughout the process, A2A supports streaming updates: agents can push incremental results or status updates via Server-Sent Events (SSE) or similar, enabling real-time feedback for long tasks.

Several communication features facilitate rich interactions:

• Capability Discovery: As mentioned, an agent’s capabilities are published in its Agent Card. A client agent can query or read this manifest to decide which agent is best suited for a given task. This helps orchestrate specialized agents – for example, an agent needing a data visualization might discover another agent that has a “chart generation” capability.
• Collaboration Messaging: Beyond formal task requests, A2A allows agents to exchange messages that carry context, intermediate results, or prompts to each other. This is essentially an agent-to-agent dialog channel to share information needed to complete tasks. For instance, an agent might send a clarifying question or partial data to another agent as they work together.
• User Experience Negotiation: A unique aspect of A2A is that message payloads can include “parts” with specified content types (text, HTML, image, audio, etc.). This allows agents to negotiate the format of output for the end-user’s UI. For example, an agent might be capable of returning a chart as an image or an HTML iframe. Through A2A, the client agent and remote agent can agree on how the artifact should be delivered so that the user’s interface can render it properly. This ensures that even if agents have different output capabilities, they can find a common ground for presenting results.

The message format in A2A is primarily JSON. According to Google’s draft spec, a Task object contains fields like an id, a description or parameters, maybe a type, and so on. Responses contain status or result data (artifacts). Error handling and cancellations are also part of the protocol (for instance, if a task needs to be aborted). A2A’s use of JSON-RPC implies that request/response bodies follow a standard schema, and the inclusion of SSE means that the remote agent can send a stream of events (e.g. progress updates or partial results) to the client agent by keeping the HTTP connection open.

A2A supports any type of agent as long as it can speak HTTP/JSON. This includes agents built with frameworks like LangChain, Google’s own Agent Development Kit (ADK), AI assistant agents, tool-using agents – essentially any agentic system can be wrapped with an A2A interface. The emphasis is that the agent is an autonomous service (a “blackbox”) that can handle tasks, as opposed to a stateless API tool. By standardizing how such agent services communicate, A2A brings interoperability to heterogeneous agent ecosystems.

Integration Options, APIs, and SDKs

Sources:

• Build and manage multi-system agents with Vertex AI | Google Cloud Blog

Since A2A is an open specification, Google has provided a draft specification on GitHub and is encouraging community contributions. At launch, A2A came with documentation and some sample implementations, but official SDKs were still in progress. Google representatives noted that the current spec and samples were early and that “official SDKs and client/servers” were actively being developed with partners. This means developers can expect libraries for different languages (likely starting with Python or Node.js) to simplify integrating A2A into their agents.

Out of the gate, Google also introduced the Agent Development Kit (ADK) – an open-source framework for building and orchestrating agents, which natively supports A2A. ADK in Python allows developers to create agents in a few lines of code and includes constructs for multi-agent collaboration. While ADK is a broader toolkit (including tools and guardrails), it uses A2A to enable multi-agent communication across frameworks . For example, an agent built with ADK can publish its skills via A2A and call out to a LangChain-based agent or a third-party agent service through the A2A interface.

Integration with existing systems is also considered. Because A2A is HTTP/JSON-based, an A2A-compliant agent could be invoked like a typical web service. The protocol can be used not only between two autonomous agents, but also by a traditional application to invoke an agent (the app would act as an A2A client). This means enterprise software or workflows can call A2A agents via REST/JSON API calls. Google’s materials even recommend modeling A2A agents as MCP resources in applications, meaning a system could use MCP for feeding data to an agent and use A2A to have that agent collaborate with others – a unified integration approach.

As for developer tooling, aside from ADK, Google and partners have begun working on A2A servers/clients reference implementations. The A2A GitHub repository indicates code for things like an example agent service and test clients. Over 50 industry partners committed to contributing, so integration plugins might emerge (for instance, connectors for A2A in existing agent frameworks such as LangChain or Flowise). The open-source nature of A2A is intended to invite broad adoption – any vendor can implement the protocol in their agent platform.

In summary, integration options for A2A include direct HTTP API calls (using JSON-RPC payloads), emerging SDKs for common languages, support through Google’s ADK and cloud (Vertex AI) ecosystem, and community-driven adapters. This makes it relatively straightforward to start experimenting with A2A by either setting up a simple web server for your agent or using Google’s examples, and then scaling up to more robust frameworks as SDKs mature.

Security and Privacy Model

Security is a first-class consideration in A2A’s design. Google specifies that A2A is “secure by default” and supports enterprise authentication/authorization schemes equivalent to those used in OpenAPI specifications. In practice, this means an A2A agent can require clients to provide API keys, OAuth tokens, or other credentials in order to accept tasks – just as a typical REST API would secure its endpoints. The Agent Card manifest includes any auth requirements an agent has (for example, it might indicate that OAuth2 is needed and provide an endpoint for obtaining tokens). This allows secure agent discovery – an agent knows how to authenticate with another if needed. The use of HTTPS for transport is implied, given that it builds on HTTP and enterprise requirements. In essence, A2A leverages standard web security practices (TLS encryption, token-based auth, etc.) to ensure that only authorized agents or users can invoke certain actions, preventing unauthorized access or misuse of an agent’s capabilities.

Another aspect of security is isolation and compliance. The protocol is designed to function even in air-gapped or highly secure environments. According to one partner comment, A2A enables “seamless, trusted collaboration—even in air-gapped environments—so businesses can innovate at scale without compromising control or compliance” . This suggests that agents can be deployed within a secure network and still use A2A to talk to each other without exposing data externally. An enterprise could run multiple A2A agents in its private cloud or on-premises; as long as they can reach each other over the network, they can collaborate. No external dependency is required beyond the protocol itself (which can run on internal HTTP servers).

Privacy considerations are addressed by giving businesses control over where agents run and what data they exchange. Because A2A is an open protocol, companies can host their own agent instances; there’s no requirement to use Google’s servers or any centralized service. Data exchanged between agents stays within the channels the enterprise sets up (e.g., within their cloud infrastructure). Moreover, A2A’s authentication model means data access can be tightly controlled – an agent only receives the information it’s authorized for. For example, a finance-reporting agent might refuse tasks from an unknown agent, or not share certain sensitive artifacts without proper credentials. This model helps maintain principle of least privilege among agents in a multi-agent system.

While A2A itself is about communication, it can be paired with existing security infrastructure. For instance, logging and monitoring can track inter-agent messages for compliance, and since everything flows over HTTP, companies can use familiar tools (API gateways, firewalls, etc.) to enforce policies. Google also notes that enterprises benefit from a “standardized method for managing their agents across diverse platforms”, which includes governance aspects. By having a uniform protocol, it becomes easier to apply consistent security rules (as opposed to each custom integration having its own ad-hoc security).

In summary, Google A2A’s security model piggybacks on proven web API security standards: encrypted channels, strong authentication, and explicit permission negotiation via agent manifests. This approach is meant to instill confidence for enterprise use, ensuring that agent cooperation does not come at the expense of data privacy or system security.

Scalability and Performance

Being geared for enterprise and cloud environments, A2A is designed with scalability in mind. Each A2A agent can be thought of as a microservice. This means you can deploy multiple instances of an agent behind a load balancer to handle higher loads, just as you would scale a web service. Because the protocol relies on stateless HTTP requests for tasks (aside from streaming connections for updates), it can naturally fit into scalable architectures (e.g., Kubernetes deployments for agents). Google’s announcement explicitly mentions leveraging their “internal expertise in scaling agentic systems” to ensure the protocol meets the challenges of large-scale, multi-agent deployments. The goal is for potentially dozens or hundreds of agents to coordinate without overwhelming complexity.

A2A’s JSON-RPC messages are lightweight and human-readable, which is good for interoperability but not the most bandwidth-optimal format. However, for most use cases (textual tasks, moderate payloads) this is not a bottleneck, and the benefits of JSON (easy debugging, integration) outweigh pure performance concerns. For high-volume binary data (e.g. video streams), A2A can support streaming those as well – likely via content negotiation to possibly use binary channels or links to external data stores if needed. The inclusion of SSE for streaming updates means that long tasks can be handled asynchronously, freeing up client resources while waiting for results.

While there are no published benchmarks yet (given the protocol’s newness), we can infer some aspects of performance: latency of agent calls will typically be similar to a standard web API call (tens of milliseconds within a data center, or higher if across the internet). If agents chain multiple calls, total latency adds up, so designing cooperative agents might involve co-locating them or planning around network delays. Throughput can be scaled by running more agent instances. One potential performance consideration is that if an agent needs to converse with many other agents simultaneously (fan-out), it may need to manage multiple HTTP connections or threads – something that the agent implementation (not the protocol itself) has to handle.

For scalability of coordination, A2A’s decentralized nature (no central broker by design; each agent has its own endpoint) means there isn’t an inherent single point of bottleneck. Discovery of agents via Agent Cards could be cached or indexed in directories if the number of agents grows large in an organization. The open protocol also allows future optimizations, such as adopting WebSocket or gRPC transports (the spec is not limited to HTTP forever if better methods emerge, as long as backward compatibility is maintained).

In summary, A2A is meant to be enterprise-scale, letting organizations “confidently deploy, orchestrate, and scale diverse AI agents, regardless of underlying technologies”. Actual performance will depend on implementation, but the protocol’s simplicity and reliance on scalable web standards position it well for high-load scenarios. As the protocol and implementations mature, we may see concrete benchmarks and performance tuning guidelines published by Google or the community.

Industry Positioning and Use Cases

Google has strategically positioned A2A as a standard for enterprise AI workflows. The protocol is backed by a who’s-who of enterprise software companies and system integrators, signaling that it is intended to be widely adopted across industry platforms. This coalition suggests use cases where, for example, a CRM vendor’s agents could directly interface with an ERP vendor’s agents, or a cloud operations agent could delegate to a security agent from another provider – all via A2A. By introducing A2A, Google is aiming to facilitate “a future when AI agents, regardless of their underlying technologies, can seamlessly collaborate to automate complex enterprise workflows” .

Use cases envisioned for A2A include:

• Enterprise Automation: Agents handling different business processes (HR onboarding, IT helpdesk, finance approvals, etc.) can coordinate. For instance, a procurement agent could trigger a finance agent to issue an invoice, then inform a logistics agent to schedule a delivery. A2A provides the lingua franca for these hand-offs. Google gave an example of hiring: an agent that finds job candidates can work with another that schedules interviews and another that updates the HR system.
• Multi-agent services (Agent orchestration across domains): In customer service, one agent might handle the customer query, but it could call a specialized shipping-status agent or a billing agent as needed (rather than having one monolithic agent attempt everything). This modular approach is made possible by A2A inter-agent calls.
• Cross-vendor AI solutions: A company might use an agent from Vendor A for scheduling and an agent from Vendor B for travel booking. A2A allows these to talk directly. This avoids vendor lock-in by ensuring agents speak a standard protocol. It’s especially relevant for enterprises that want best-of-breed agents from different sources.
• Complex problem solving and delegation: Agents can delegate subtasks to other agents via A2A. For example, a research agent working on a complex problem could enlist a data-analysis agent to crunch numbers and a visualization agent to produce charts, then compile the results. Each agent is specialized, and A2A handles the communication glue.
• IoT and operations: Although not limited to classic “LLM agents,” A2A could also unify communication between AI agents controlling physical or network systems. For example, a datacenter management agent might communicate with a cooling system agent and a load-balancing agent to optimize operations collaboratively.

From an industry perspective, Google’s launch of A2A also positions it somewhat in competition or contrast to Anthropic’s MCP, sparking talk of “AI agent protocol wars”. However, Google stresses complementarity: MCP is about connecting agents to tools/data, whereas A2A is about agents talking to each other . In practice, an enterprise might use both: MCP to let agents fetch data or use APIs, and A2A to let multiple agents coordinate decisions.

Google’s timing and partnerships indicate they want A2A to become the de facto standard for multi-agent interoperability, especially in enterprise and cloud environments. If widely adopted, A2A could be as ubiquitous as protocols like HTTP or REST for services, but specifically tuned for AI agent interaction. It’s intended not just for Google’s own ecosystem (though naturally Google Cloud services and the Vertex AI platform are integrating A2A support), but as an open industry standard – hence the open-source spec and multi-company involvement.

Early Feedback and Developer Commentary

Given A2A’s newness, early reactions have been a mix of optimism and skepticism in developer communities. On one hand, many applauded Google for addressing a real need – the lack of standardization in agent communication – and for releasing A2A as an open-source project with clear documentation. Observers noted that A2A’s documentation and explanation were quite clear, especially when compared to Anthropic’s MCP (which some found less accessible). This clarity and Google’s backing led some to view A2A as a possible “superset” of MCP that could eventually encompass more functionality.

On the other hand, some developers questioned whether A2A truly provides new value over existing approaches. In online discussions, a few commenters expressed uncertainty about what A2A achieves that couldn’t be done with MCP or even simpler APIs. They wondered if this might introduce yet another standard to reconcile. Google’s team responded in forums, clarifying that A2A is still evolving and that they intentionally released it early to incorporate community feedback. They emphasized that A2A is not yet a finished product but a starting point to build upon in the open.

Another point of discussion was the overlap or potential conflict with IBM’s ACP and other protocols. Industry analysts have speculated that we may see a convergence or competition between these standards. Some characterized Google’s move as potentially igniting a “protocol war” in the AI agent space, though it’s also noted that A2A and ACP currently target somewhat different layers (as we will discuss, ACP is focused on IBM’s BeeAI environment initially). In general, the early commentary recognized that multiple protocols (MCP, A2A, ACP) emerging around the same time is evidence of a trend: the community sees formalizing agent communication as crucial. This has been compared to past tech battles like VHS vs Betamax or XML vs JSON – in time, one standard may dominate or different standards may find different niches.

So far, concrete hands-on evaluations of A2A are limited (it’s only been available since April 2025). Developers experimenting with the spec have reported that it’s straightforward to get a basic agent service running and that using JSON-RPC over HTTP felt familiar. The true test will come with building larger systems: how well does A2A handle many agents, complex negotiations, or error cases? Those results will become clearer as more people build with it. Google’s openness to feedback and the broad partner support suggest that A2A could rapidly improve and iterate based on this early usage.

In summary, early feedback on A2A is cautiously positive – it’s seen as a timely solution with strong backing, though its long-term success will depend on adoption and proving its worth in real-world agent systems. Many are watching closely as A2A and competing protocols develop, given the high stakes of establishing the “language” in which future AI agents will speak.

IBM Agent Communication Protocol (ACP)

Sources:

• Introducing multiagent BeeAI - IBM Research
• IBM’s ACP is an ‘Extension’ of Anthropic’s MCP | AIM Media House
• Introduction - BeeAI
• Architecture - BeeAI
• ACP (Agent Communication Protocol) · i-am-bee · Discussion #284 · GitHub

Purpose and Goals

IBM’s Agent Communication Protocol (ACP) is an initiative by IBM Research aimed at standardizing how AI agents talk to each other and to orchestrators, with an emphasis on integration of open-source agents across various frameworks. The motivation behind ACP is similar to A2A – current multi-agent systems suffer from each agent having a different interface or API, making it hard to get them to cooperate. ACP’s goal is to provide a “universal connector” so that agents can exchange information and coordinate actions easily, regardless of how they were built.

ACP was developed as part of IBM’s BeeAI platform, an experimental environment for running multiple AI agents together. BeeAI is designed to let developers mix and match agents from any source (any programming language or agent framework) and orchestrate them within one system. In that context, ACP’s purpose is to remove the friction between these agents – enabling them to discover each other, delegate tasks, and collaborate in a standard way. IBM cites that “agent-to-agent communication is challenged by inconsistent agent interfaces” today, and ACP is meant to overcome that.

An important aspect of ACP’s origin is that it builds upon Anthropic’s MCP. IBM describes ACP as an “extension” of MCP – leveraging MCP’s context-sharing mechanisms (for tools and data) but adding the notion of agent-to-agent interaction as first-class. Essentially, MCP (introduced in late 2024) provided a way for agents to access resources and tools in a uniform manner. IBM realized that beyond tool usage, agents also need to talk to each other, and that MCP’s structure wasn’t fully suited for that. Thus, ACP’s goal expanded: incorporate the strengths of MCP (common ways to represent context, prompts, etc.) while explicitly introducing agents as primary actors in the protocol.

IBM’s stated goal is for ACP to eventually become a standalone standard optimized for agent interactions, diverging from MCP where needed. Initially, though, ACP uses MCP as a foundation so developers don’t have to reinvent the wheel for things like connecting to data sources. Over time, IBM plans to “address misalignment” by adjusting the protocol specifically for robust agent-to-agent communication. The end goal is similar to Google’s: promote interoperability and collaboration across agent-based ecosystems. However, IBM’s approach is somewhat more experimental and community-driven at this stage, focusing on proving out useful features in BeeAI first, then standardizing them once their value is validated.

In summary, ACP’s purpose is to enable seamless multi-agent workflows (particularly in BeeAI) by standardizing interactions. It aims to simplify integration (so developers can plug in agents easily) and foster effective collaboration between agents. While A2A is positioned for broad industry adoption, ACP is initially tied to IBM’s platform and the open-source community, with a vision that successful ideas could influence a wider standard. The goal is to support the burgeoning “AI agent boom” with a solid communication backbone so that the promise of agents working together can be realized without each project writing custom glue code.

Technical Architecture and Design Principles

IBM ACP’s architecture is closely linked to the BeeAI platform architecture. In BeeAI, there is a central component called the BeeAI Server, which manages all the running agents and mediates communication between them. Unlike A2A’s fully distributed model, ACP initially follows more of a hub-and-spoke architecture: multiple agents (which could be separate processes or services, possibly on one machine or local network) register with the BeeAI Server. The BeeAI Server acts as an orchestrator, spawning or shutting down agents as needed, and provides a unified API endpoint for clients (which could be user interfaces or external systems) to interact with this multi-agent system. In essence, BeeAI Server is the central router through which agents communicate. An external application doesn’t call each agent directly; it calls the BeeAI server, which then routes requests to the appropriate agent internally, using ACP as the communication protocol between the server and agent processes.

Design principles behind ACP include:

• Local-First and Framework-Agnostic: BeeAI (and by extension ACP) is designed so that all agents can run locally (e.g., on a developer’s laptop or on a private server) for full data control. This means the architecture doesn’t assume cloud services – you can run it on-premises, which appeals to privacy. Also, “any framework or coding language” is supported – ACP is language-neutral, as agents communicate via a protocol (likely JSON/HTTP as well).
• Leverage MCP for Tools/Data: In its design, ACP inherits from MCP the way to represent resources, tools, and context. This means an ACP agent can use MCP-compatible interfaces to fetch data or invoke tools. By using this existing standard, ACP avoids reinventing that part of the wheel.
• Agent Discovery and Task Delegation: ACP specifically introduces mechanisms for agents to discover other agents and delegate tasks to them. This is a core design goal – enabling a registry or catalog of agents (BeeAI even has an Agent Catalog of available agents). Agents can advertise their capabilities (similar to A2A’s Agent Card concept, ACP discussions mention a manifest for offline discoverability) so that other agents or the BeeAI orchestrator can find an appropriate agent for a task.
• Iterative, Community-Driven Development: IBM’s approach with ACP is to implement practical features first in an alpha stage, and then standardize them once proven. They explicitly avoid locking down the spec too early. For example, they started with an alpha draft and invited open-source community participation on GitHub to shape it. This means the design is somewhat fluid, focusing on what developers find useful in multi-agent orchestration, such as state management, error handling, etc., and evolving through feedback.
• Deep Telemetry and Traceability: Being an IBM Research project, ACP (via BeeAI) puts emphasis on observability – tracking agent interactions, performance, and outcomes. BeeAI has built-in telemetry that can integrate with tools like Arize Phoenix for monitoring. So the design includes hooks for logging and analyzing agent behaviors. This principle is about ensuring that when multiple agents talk to each other, there’s visibility (important in enterprise settings for debugging and trust).

The architecture in practice: When BeeAI is running, an agent (say an open-source coding assistant agent) is wrapped in a container or adapter that speaks ACP. The BeeAI server might keep a registry of active agents, each identified by name, version, capabilities. If one agent needs something from another, it either goes through the server or uses the server to look up the other agent’s address. Communication then happens likely over HTTP or an internal messaging system defined by ACP. Notably, IBM’s documentation mentions the BeeAI Server provides a REST API for agent communication. This implies that ACP might be implemented as RESTful endpoints where one agent (or the server on its behalf) calls an endpoint representing another agent’s action. In fact, IBM released an OpenAPI (Swagger) specification for ACP’s alpha version, suggesting a REST/JSON approach as opposed to JSON-RPC. For example, there might be an endpoint like POST /agents/{agentId}/tasks to send a task to a given agent, etc., defined in that OpenAPI spec.

ACP’s architecture also allows integration with third-party agent frameworks. BeeAI can integrate with external frameworks (like LangChain or others) via providers or adapters. This means an agent written for LangChain could be run under BeeAI, and ACP would handle its communication. The design principle here is pluggability – accommodate different agent implementations under one protocol.

In summary, IBM ACP’s architecture is slightly more centralized (in current form) with BeeAI as the orchestrator, and focuses on the infrastructure around agents – discovery catalogs, lifecycle management (spawn/stop agents), and monitoring – in addition to the communication itself. The design principles stress compatibility (with MCP, with various agent types), local control, and evolving features with community input. As ACP matures, it may become more distributed (the team has discussed possibly peer-to-peer or WebSocket communication between agents in future), but at this alpha stage, it’s anchored by the BeeAI server component as the communication hub.

Communication Mechanisms, Message Format, and Supported Agent Types

ACP standardizes communication in a multi-agent environment by defining how agents express tasks and responses, similar in spirit to A2A but implemented within the BeeAI context. Initially, ACP communication is likely implemented over HTTP with JSON, given the release of an OpenAPI schema (indicating defined REST endpoints for the protocol). Each agent in BeeAI has an interface that the BeeAI server (or other agents via the server) can call. The message format would be JSON objects representing tasks, results, errors, etc., analogous to how MCP defines resources and tools. In fact, because ACP leverages MCP’s constructs, some message types might include MCP-like context blocks (prompts, tool invocations), but extended to address an agent as the executor of a task.

While detailed ACP message schemas aren’t fully published in narrative form, key elements of ACP communication (as gleaned from discussions and documents) include:

• Agent Manifest / Card: To facilitate discovery, ACP will have agents provide a manifest of their capabilities and how to invoke them. IBM’s community discussions explicitly list “Manifest-Based Agent Offline Discoverability” as a topic. This is analogous to the Agent Card in A2A, and would allow an agent to advertise what tasks it can perform, expected inputs/outputs, and any requirements. BeeAI’s Agent Catalog likely uses such manifests to list available agents.
• Task Delegation: In ACP, one agent (or a client application) can delegate a task to another agent. This might be done by sending a JSON payload describing the task to the BeeAI server with a target agent in mind. The protocol would define a Task object (possibly similar to A2A’s concept) with fields such as an action name, parameters, maybe a requesting agent ID, etc. The target agent will work on the task and eventually return a result. ACP likely defines task lifecycle states as well, given tasks could be long-running. IBM’s interest in “handling stateful vs stateless agents” implies ACP must manage whether an agent maintains context between tasks or not.
• Communication Channels: ACP is exploring various transports – listed topics include HTTP, WebSockets, peer-to-peer, streaming, etc. . Currently, the simplest implementation is HTTP requests (synchronous calls) combined with server-sent events or WebSocket for streaming results back (this is something to be implemented if not already). BeeAI being local-first might allow more direct inter-process communication (IPC) for agents on the same machine, but to keep it general, they stick to network protocols. Streaming data (for example, if an agent is reading a large file or providing a live feed) is a use case they have identified to handle.
• Unified Endpoint: The BeeAI server provides a single endpoint (or set of endpoints) that external clients can use to interact with the agent society. For instance, an external UI could send a request to BeeAI server saying “have agent X do Y task”. Internally, BeeAI uses ACP to route that to agent X and get the response. Also, if agent X needs agent Y, it similarly goes through BeeAI (or potentially directly if allowed) using ACP calls. This architecture means from an external perspective, you talk to one API (BeeAI’s API), and internally ACP messages coordinate the agents.
• Supported Agent Types: ACP is meant to accommodate any type of agent, as long as you can wrap it to speak the protocol. BeeAI currently includes examples like a coding assistant agent (Aider), a research assistant (GPT-Researcher), and even an agent that turns research into podcasts – these are quite different in function and were built in different languages. ACP can support agents that are essentially wrappers around large language models (LLMs with a goal), agents that use classical algorithms, or hybrid ones. Some agents might be more tool-like (perform a single function), but if they are running as an autonomous service, they still count as agents in ACP’s view. IBM specifically highlights integrating open-source agents regardless of framework or codebase, so an agent could be written in Python, JavaScript, etc. with an adapter for ACP. The ACP SDKs (discussed next) help create these adapters for different languages.

The message content in ACP likely includes natural language context as well, given these agents often rely on LLM reasoning. The ACP discussions mention evaluating “Natural Language as an Agent Interface”. This suggests ACP might allow agents to communicate in natural language in some cases (like sending a request in plain English for the agent to interpret). However, more concrete tasks would be structured (like JSON commands). Perhaps ACP’s flexibility is that an agent could send either a structured request or even a freeform message, and it’s up to the receiving agent to handle it – but this is speculative. At minimum, ACP will standardize the structured part (task schemas), and agents can always include a field that has a natural language prompt if needed.

In summary, ACP’s communication mechanism is currently an HTTP+JSON-based API that BeeAI uses to route messages between agents. It incorporates the notion of an Agent as a service (with a manifest and available operations) and standardizes how tasks are invoked and results returned. Like A2A, ACP supports asynchronous operation and streaming, though those features are under active development. The set of supported agent types is broad – essentially any autonomous component can become an ACP agent with the right wrapper. IBM’s initial focus is on known open-source agents (to bring them into the fold easily), but the protocol would apply equally to proprietary or new agents, especially if BeeAI moves beyond the lab into enterprise use.

Integration Options, APIs, and SDKs

IBM provides integration with ACP primarily through the BeeAI platform. There are a few layers to integration: the BeeAI CLI and UI, the ACP SDKs, and the REST API of BeeAI.

• BeeAI CLI and UI: BeeAI includes a command-line interface and a web UI that allow developers to discover available agents, launch them, and compose them into workflows. Through these tools, a developer can integrate agents without writing much code – for example, using CLI commands to run a certain agent and connect it to another. This is more of a user integration than a programmatic one, but it’s part of how IBM envisions developers experimenting with multi-agent setups. The CLI/UI themselves use ACP under the hood to manage agents.
• ACP SDKs: As part of the alpha release, IBM has SDKs in Python and TypeScript for ACP. These libraries help developers implement ACP in their agents or connect to BeeAI’s ACP interface. For instance, a Python SDK might let you register your agent with the BeeAI server, handle incoming task requests, and send requests to other agents easily. The TypeScript SDK could be used for building web applications or Node.js agents that interact with BeeAI. Having SDKs in these languages covers a lot of ground (Python is popular for AI agents; TS/JS could be used for front-end or service integration). Over time, more SDKs could emerge, but these two show IBM’s focus on open-source agent communities (many are Python-based) and integration with web tech.
• REST API (OpenAPI): The BeeAI server exposes a RESTful API, documented via OpenAPI, that allows external systems to interact with the multi-agent system. This means if you’re a developer who doesn’t necessarily use the SDK, you can still send HTTP requests to BeeAI to do things like list available agents, send a task to an agent, or retrieve results. The OpenAPI spec published (as a draft) presumably details endpoints such as /agents (GET to list, POST to register?), /agents/{id}/tasks (to send tasks), etc. This makes integration with ACP possible from any platform that can make HTTP calls, including languages where an SDK might not exist yet. For example, a Java application could integrate by calling these APIs directly.
• Integration with MCP and Tools: Since ACP builds on MCP for tool integration, BeeAI’s system can also integrate with MCP servers. This means if an agent needs to use an external tool (like querying a database or calling an API), it might do so via an MCP interface. BeeAI can thus serve as an MCP host as well, bridging between MCP and ACP. This is relevant if an ACP agent needs data; it could fetch via MCP and then share via ACP to another agent. So, in terms of integration, if a developer already has MCP set up with certain tools, they could integrate those into BeeAI so that all agents have access to them. Conversely, BeeAI could present its agents as resources to an MCP system, though that’s more speculative until ACP fully diverges from MCP.
• Launch and Deployment: IBM’s vision is that BeeAI (with ACP) can run on a developer’s local machine or on private infrastructure, meaning integration into pipelines or environments is flexible. A team could include BeeAI as part of their development stack – e.g., spin up BeeAI in a Kubernetes cluster, load it with agents, and use its API to perform tasks. The local-first approach ensures that even without cloud connectivity, agents can function and integrate (useful for secure environments).
• Community and GitHub: IBM has open-sourced parts of BeeAI and ACP (the documentation, discussions, and likely some code). They invite developers to contribute ideas on GitHub. This means integration is not just “you using IBM’s tool” but potentially you modifying or extending it. For instance, if someone wanted ACP to support a new transport, they could propose it in the community. This open development model can encourage faster integration of ACP with other emerging agent frameworks. We might see community-contributed adapters for popular agents to speak ACP, or even plugins that allow BeeAI to interface with other orchestrators.

In practice, if you want to use ACP as of now, you would likely: install BeeAI (which comes with the server, CLI, maybe some default agents), use the Python SDK or CLI to add any custom agent you have (writing a small wrapper if needed to handle ACP calls), and then use the BeeAI UI or API to run tasks. If you wanted to integrate an existing application with this agent swarm, you’d call the BeeAI REST API to issue tasks and get results. IBM’s near-term focus is on developers experimenting with this rather than production deployment, so integration is geared towards flexibility and ease for dev/testing.

As ACP matures, one could envision more direct integration without the full BeeAI. For example, agents outside BeeAI might speak ACP to talk to agents inside BeeAI, if BeeAI’s server allowed external agent connections. IBM also might collaborate with cloud teams to integrate ACP into IBM’s cloud offerings or enterprise software (for instance, adding multi-agent capabilities in IBM’s Automation suite using ACP). But those are forward-looking possibilities. For now, integration with ACP is largely synonymous with using the BeeAI experimental platform and its tools, with open-source SDKs facilitating connecting your agents or apps to that ecosystem.

Security and Privacy Model

At this early stage, IBM’s ACP has less publicly detailed about security compared to Google’s A2A, but some aspects can be inferred from its design and IBM’s general approach:

Authentication and Access Control: Since ACP in BeeAI often runs locally or on private infrastructure, the immediate security model is that it’s under the user’s control – if you can run BeeAI, you presumably have the rights to manage the agents. However, as it evolves into a standard, ACP will need authentication similar to MCP’s or A2A’s if it’s used in networked environments. The agent manifest in ACP would include any auth requirements for accessing that agent’s capabilities (similar to how MCP tools might require API keys). The BeeAI server’s API itself can be secured via typical web auth (for example, requiring an API token to send tasks if exposed as a service). IBM hasn’t explicitly published details, but we do know they consider MCP as an interim solution and plan to optimize ACP for agent interactions, which likely includes optimizing security for inter-agent calls.

One could expect ACP to eventually support token-based authentication for agents: e.g., an agent might only accept tasks from certain trusted peers or if a valid token is presented. Within BeeAI’s local context, this might not be needed (all agents are essentially under one user’s control), but in a broader distributed scenario, it becomes important. IBM inviting open discussion on “roles and responsibilities” of agent providers and MCP servers indicates they are thinking about identity and roles in these protocols – which is related to security (who is allowed to do what).

Encryption and Network Security: Running locally means communication can be on loopback interfaces or LAN, but if ACP were used across machines, one would use HTTPS or similar to encrypt agent communication. Given IBM’s enterprise focus, any official deployment of ACP would likely enforce TLS encryption for any agent messages over a network. In BeeAI’s current form, if all agents are local processes, security is more about process isolation (which is not a concern if you trust all code you run).

Privacy and Data Control: A selling point of BeeAI is “full data control” by running agents locally. This inherently is a privacy advantage – sensitive data handled by agents doesn’t have to leave your machine or private cloud. IBM likely sees this local-first approach as a way to appeal to enterprises concerned about sending data to third-party services. So ACP supports privacy by enabling an on-premises multi-agent setup rather than requiring a cloud service. Also, because ACP plans to allow integration of externally hosted models (another item in the motivation: “model dependency management”), it means you can keep certain models or data in-house and still have agents communicate about them.

Authorization and Agent Permissions: In multi-agent systems, it’s important to manage what one agent is allowed to ask of another. While not explicitly stated, IBM’s enterprise mindset suggests that ACP could incorporate permissioning. For example, an admin could configure that Agent A is allowed to call Agent B’s “sendEmail” action, but Agent C is not. This might be handled outside the protocol initially (simply not connecting certain agents together), but a mature standard might include metadata for permissions.

Security vs MCP: Since ACP extends MCP, it inherits some security considerations of MCP. Anthropic’s MCP included the idea of capability-based access (tools presented to models in MCP can be sandboxed or filtered). ACP, focusing on agent communication, might similarly ensure that when an agent delegates a task, it does not inadvertently give away more authority than intended. For example, if an agent asks another to perform a task, the receiving agent shouldn’t be able to do more than what the requesting agent itself could do, unless authorized. IBM’s mention that MCP’s original design is an imperfect fit for agent comms could include refining these security boundaries (since agents are active entities, not passive tools).

In summary, IBM ACP’s security model is still shaping up. In the BeeAI alpha, the environment is controlled (likely no elaborate auth because it’s assumed to be a dev setting). But for the broader vision, ACP will need to incorporate strong authentication and encryption akin to other web APIs. IBM’s advantage is extensive experience in enterprise security, so we can expect that as ACP moves out of experiment phase, things like enterprise SSO integration, encrypted agent channels, audit logs, and fine-grained access control will be considered. Privacy-wise, ACP’s design of local operation gives organizations the option to keep agent interactions completely within their secure walls, which is a compelling feature for sensitive applications.

Scalability and Performance

IBM’s ACP, being in pre-alpha/alpha, has less in the way of stated scalability benchmarks, but we can analyze the intended use and architecture for performance implications:

Local Orchestration and Scale: BeeAI’s initial use case is a developer running multiple agents on one machine. This could be a powerful machine (with multiple GPU/CPUs) running, say, a dozen agents concurrently. The BeeAI server can manage these processes, but the scalability is currently bounded by the single host (in the local-first scenario). For larger scale, BeeAI could be deployed on a server or cluster. The notion of running on “private infrastructure” suggests you could deploy BeeAI in a distributed manner (maybe multiple agent hosts with one central coordinator). If BeeAI and ACP evolve, we might see the BeeAI server itself scale out or federate (multiple BeeAI instances communicating). At this moment, the simplest path to scale an ACP setup would be to run it on a more powerful node or manually set up multiple BeeAI servers for different groups of agents.

Performance of Agent Calls: If ACP is using REST/HTTP internally, each inter-agent call has overhead similar to a local API call. On a single machine, this might be negligible (could even be loopback network calls or some IPC optimizing it). The latency would be low (milliseconds). If agents are running on the same machine and communicating, context-switching and process communication are the main overhead. IBM likely isn’t focusing on micro-optimizations yet, given it’s early – correctness and features come first. But over time, if ACP sees heavy use, they might consider more efficient transport (e.g., using binary serialization, or a message bus for agent communication to avoid HTTP overhead for internal calls).

Task Management and Concurrency: BeeAI server handling multiple agents means it can sequence or parallelize tasks. For example, if Agent A and Agent B are both busy, the server might queue incoming tasks. There might be a scheduling mechanism. The performance here depends on how the BeeAI server is implemented (which we don’t have details on). It likely can handle multiple threads or async calls to agents. If an agent is busy with a long task, BeeAI might allow it to stream updates, which is fine. The throughput of tasks in an ACP system will depend on how many tasks each agent can handle in parallel and how the server routes them. This area would benefit from performance tuning as the project matures. Possibly IBM will measure how many tasks per second the BeeAI orchestrator can dispatch or how many concurrent agent interactions it supports and optimize accordingly.

Scalability through Community Agents: One interesting performance angle is that BeeAI can incorporate lighter weight agents (some could be simpler scripts or smaller models) and heavier ones (big LLMs). The ACP standard will need to handle both ends – e.g., a simple agent might respond very fast, whereas a complex one might take a while or require heavy compute. The protocol has to not bottleneck the faster interactions while waiting for slower ones. The inclusion of telemetry suggests IBM is mindful of monitoring performance. If one agent becomes a laggard, you’d see it in traces.

No explicit benchmarks yet: As expected, IBM hasn’t published metrics like “ACP can handle X agents with Y QPS”. But they did highlight the scenario of having “a dozen or more” agents working together, implying they are at least testing with tens of agents. Over time, if ACP is to compete or align with A2A, it would need to scale beyond that (to perhaps hundreds of agents in a production scenario).

One must also consider scalability of development: since ACP is open-source alpha, its ability to scale in features and stability will depend on community and IBM’s resources. IBM is actively encouraging community involvement, which can accelerate solving performance issues as more eyes look at the code.

In conclusion, ACP’s scalability and performance are currently adequate for experimental and moderate use (small multi-agent teams), with the roadmap likely targeting improvements. A key difference from A2A is that A2A from the outset imagines a cloud-scale distributed scenario, whereas ACP’s current implementation is optimized for a controlled environment (which can then be extended outward). Should BeeAI/ACP transition into an enterprise product, expect IBM to ensure it can handle enterprise workloads (which might include clustering the BeeAI server, load balancing agent calls, etc.). For now, ACP is about proving the concept with manageable-scale deployments and using those learnings to inform a more scalable design.

Use Cases and Industry Positioning

IBM’s ACP is part of a broader vision IBM has for AI agents, but it’s being incubated in a research/experimental setting rather than immediately pushed as an industry standard. The primary use cases for ACP align with BeeAI’s capabilities:

• Developer Experimentation with Multi-Agent Systems: IBM revamped BeeAI in early 2025 to focus on developers, making it easier to find and plug in various open-source agents. ACP serves these users by giving them a common interface to have those agents talk to each other. A developer could, for example, take an AI coding assistant and a data analysis agent, and have the coding assistant ask the analysis agent for insights at runtime – all done via ACP calls in BeeAI. This lowers the barrier to orchestrating agents, as the developer doesn’t need to custom-write the integration between them.
• Enterprise Prototyping and Demos: BeeAI’s earlier incarnation was aimed at business users, and while the pivot is to devs, the ultimate aim is to solve problems businesses have with multi-agent systems. IBM might target use cases like assembling agents for a specific business workflow. For instance, a company could prototype an “AI helper team” where one agent reads incoming emails, another drafts responses, and another updates a database, all coordinated in BeeAI. ACP would handle the info flow between these. This is somewhat similar to A2A’s enterprise workflows use case, but done in a contained environment.
• Integration of Heterogeneous Agents: A strength of ACP/BeeAI is running agents from different projects. Think of a scenario: an NLP agent from one open-source project, a computer vision agent from another, a planning agent from IBM’s own research, etc. Instead of building one giant agent that does all, you use specialized ones and let them communicate. ACP is designed for this “mix-and-match” use case, making it easier to reuse existing agents. Over time, as more agents become ACP-compatible, an organization could have a library of agents to deploy as needed.
• Research on Agent Collaboration: Since this is an IBM Research endeavor, one use case is actually to study how agents can collaborate. By having a standardized protocol, researchers can more easily set up experiments with multiple agents to see emergent behaviors, test cooperation strategies, etc. ACP provides a controlled environment to do that, with telemetry to observe what happens. The data from these experiments could inform best practices for multi-agent systems in general.
• IBM Ecosystem Use: In the long run, IBM might integrate ACP into its products or offerings. For example, IBM has Watson and other AI services; ACP could allow an IBM Watson agent to work with an open-source agent. Or in IBM’s automation software (like IBM’s business process tools), ACP could coordinate AI assistants for different steps of a workflow. So, a use case could be within IBM’s consulting or solutions: delivering a tailored multi-agent solution to a client using BeeAI/ACP under the hood to integrate everything.

In terms of industry positioning, IBM ACP is not as loudly announced to the market as Google’s A2A. It was introduced via IBM Research blog and targeted media, which frames it as a work in progress and community project. IBM is essentially saying: “We see the need for agent communication standardization too, and we’re building on prior work (MCP) to push it further.” By calling ACP an extension of MCP, IBM positions itself as collaborating in this space rather than directly competing with Anthropic or Google… yet. IBM even acknowledges MCP as an “interim solution” and clearly intends to influence the direction of agent standards by contributing their ideas. This implies IBM wants a seat at the table for defining how agents interact, likely to ensure any eventual standard meets enterprise requirements and perhaps aligns with IBM’s strengths (integration, on-prem solutions, etc.).

IBM’s focus on open-source agents also positions ACP somewhat differently: it’s rooted in the open-source AI agent movement (AutoGPT, LangChain agents, etc.). By making those easier to orchestrate, IBM can ingratiate itself with the developer community and possibly steer them towards IBM’s tools. It’s a more grassroots approach compared to Google’s top-down industry coalition. If ACP gains traction among developers tinkering with agents, it could become popular in open-source projects.

Intended use cases vs A2A: ACP for now is used within BeeAI – so scenarios often involve orchestrating agents in a controlled environment (maybe behind a firewall, within one org’s context). A2A envisions cross-organization agent communication (e.g., SaaS services from different vendors talking). IBM might eventually go there, but initially ACP is about making your agents work together better. For example, if a bank is developing several AI assistants for different departments, IBM ACP/BeeAI might be used internally so those assistants can collaborate (without necessarily exposing an API to outside agents).

In summary, IBM ACP’s use cases revolve around enabling multi-agent orchestration in a contained, developer-friendly platform, with eyes on broader enterprise applicability once matured. The industry positioning is that IBM is contributing to the formation of agent communication standards, ensuring that the eventual solutions align with things like open-source adoption, on-premises deployment, and rich integration/telemetry – areas IBM has long been involved in. It’s a somewhat quieter, more experimental approach compared to Google’s splashy announcement, but it addresses similar fundamental needs in the emerging multi-agent ecosystem.

Early Feedback and Analysis

Since ACP was introduced as a draft/alpha (and primarily to a technical audience), there isn’t as much public commentary as with Google’s A2A. However, a few points of analysis have emerged:

• Alignment with MCP: Many observers noted that IBM’s ACP is building directly on Anthropic’s MCP, essentially validating MCP’s core concepts but extending them. Analytics India Magazine quoted IBM describing how “MCP provides essential mechanisms for sharing context … ACP leverages these… while explicitly adding the concept of agents as primary participants.”. Early analysis often frames ACP as complementary to MCP, not a reinvention. By acknowledging MCP as a starting point, IBM got some nods for not fragmenting the space unnecessarily (at least initially). That said, IBM’s plan to diverge from MCP for a standalone agent-focused standard means down the line there could be a separate path. Some tech bloggers view ACP’s evolution as a sign that MCP alone wasn’t sufficient and that IBM is stepping in to fill the gap (with the experience of BeeAI’s needs guiding it).
• IBM’s Agent Strategy: Tech commentators have placed ACP within IBM’s broader AI strategy. IBM has been active in promoting trustworthy AI, AI for business, etc., and BeeAI/ACP is seen as an extension of that – providing the plumbing needed for trustworthy multi-agent systems. There is some early optimism that IBM’s involvement (with their enterprise know-how) could lead to a robust standard that enterprises feel comfortable with. However, there is also caution: IBM’s history with trying to set standards (like OpenPOWER, etc.) has been mixed, and Google’s entrance with A2A could overshadow IBM’s effort if the community gravitates more to A2A. Some developers on social media questioned whether ACP will simply be folded into A2A eventually, or vice versa, to avoid having two similar standards.
• Pre-alpha Maturity: IBM themselves label ACP as “pre-alpha” or alpha, which signals it’s not production-ready. Early users (those who tried BeeAI’s new version) likely found rough edges. IBM openly stated features like agent discovery, task delegation, etc. are still evolving . The community discussion threads on GitHub have dozens of comments deliberating fundamental questions (like what is the best data encoding, how to handle state). This transparency is great for development, but it also shows that ACP is a work in progress. Early feedback from these discussions is essentially helping shape ACP. For example, suggestions about using natural language between agents or how to implement cancellation of tasks are being debated. This means any “analysis” of ACP’s capabilities has to be understood as subject to change. What we see now is the direction of ACP rather than a fixed set of features.
• Comparisons to A2A: Inevitably, as soon as A2A was announced, people compared it to ACP (and MCP). A consensus view in early analysis is: ACP is more focused on orchestrating multiple agents within a single system (like BeeAI), whereas A2A is aimed at interoperability across systems. WorkOS, for example, summarized that “ACP’s current sweet spot is orchestrating agents within BeeAI. A2A is more direct if you want cross-framework communication outside the BeeAI ecosystem.”. This suggests that in the near term, these protocols might not directly clash because they serve slightly different purposes – one could even imagine BeeAI adopting A2A to talk to external agents while using ACP internally. In fact, some have speculated that BeeAI might in the future support A2A endpoints as a way to interface with other agent platforms, bridging the two worlds. IBM hasn’t announced that, but the idea is floated in community discussions.
• Developer interest: It appears that interest in ACP is tied to interest in BeeAI. BeeAI’s initial launch (mid-2024 for business users, then refocus in 2025 for devs) didn’t get as much hype as, say, the AutoGPT trend, but IBM is trying to tap into that energy. Some AI enthusiasts have tried BeeAI and reported it’s an interesting way to test multiple agents. If BeeAI gains a following, ACP will get more direct feedback. So far, the feedback loop is small but engaged (GitHub issues, IBM’s discord, etc., for BeeAI). The fact that IBM is inviting developers to contribute means they are listening, and early contributors seem enthusiastic about building a standard rather than everyone making their own.
• Enterprise readiness: Analysts writing about ACP (like in AIMedia or LinkedIn posts) often note that IBM’s involvement signals that 2025 will see rapid developments in AI agent standards. There’s commentary that with Google, Anthropic, and IBM all in the game (and possibly Meta or others soon), this space is heating up. IBM’s ACP is viewed as a pragmatic approach, building bridges rather than competing head-on at first. But some also caution that having multiple protocols could confuse would-be adopters – early enterprise adopters might hold off until it’s clearer which standard will “win” or how they interoperate. IBM’s move could be partly to ensure they have influence if an eventual convergence or standardization effort (maybe via a standards body) happens. By having ACP out there, IBM can say they have technology and experience in agent comms when talking to enterprise clients in 2025.

In summary, early analysis of IBM ACP acknowledges it as an important piece of the multi-agent puzzle, albeit one that’s in an early stage with limited real-world usage yet. It’s often mentioned in the same breath as MCP and A2A, with distinctions drawn about its focus (BeeAI’s internal coordination) and its potential (if IBM and the community flesh it out, it could become a robust standard). The tone around ACP is cautiously hopeful – IBM’s seriousness in this domain is a positive sign, but the outcome in terms of adoption and impact will depend on how fast and well ACP matures, and how it coexists or converges with the likes of A2A.

Comparative Analysis: Similarities and Differences

Both Google A2A and IBM ACP emerged almost simultaneously, reflecting a common recognition of the need for agent interoperability. They share some high-level similarities but also diverge in important ways. Below is a comparative summary of key points:

• Vision and Purpose: Both protocols seek to standardize agent communication to enable multi-agent collaboration and interoperability. Each aims to break down silos between agents. However, Google’s A2A is positioned as a broad industry standard for cross-vendor agent interaction, whereas IBM’s ACP started as a more platform-specific solution (BeeAI) aimed at integrating open-source agents (though IBM intends for it to grow into a wider standard). In essence, A2A targets a new era of agent interoperability across the enterprise world , while ACP targets making multi-agent systems feasible and easy in controlled environments (with plans to influence the broader standards landscape).

• Architecture: A2A follows a decentralized, peer-to-peer architecture for agents. Any agent can be a server exposing an endpoint and any agent can be a client – effectively a mesh of agents communicating over HTTP. Discovery is distributed via agent cards. In contrast, ACP (in its current form) uses a central orchestrator (BeeAI server) to which agents connect. Communication is routed through this hub, and a unified API is exposed to the outside world. This means A2A naturally fits a cloud or inter-company scenario, while ACP’s design is optimized for intra-system or on-premises scenarios. The trade-off: A2A’s distributed model is more flexible for connecting arbitrary agents but may require more effort in discovery and management; ACP’s centralized model simplifies orchestration and monitoring at the cost of an extra layer and potential single-point-of-failure (the orchestrator).

• Communication Protocol: Both A2A and ACP rely on web technologies and JSON for messaging, ensuring language/framework neutrality. A2A explicitly uses JSON-RPC over HTTP for requests and Server-Sent Events for streaming responses. ACP appears to use a RESTful API approach (OpenAPI-defined endpoints) over HTTP, which similarly carries JSON payloads. Neither mandates using a specific network port or transport beyond HTTP, and both consider enhancements like WebSocket or other encodings in future. Importantly, both share concepts like a task lifecycle (states of a task, long-running task support) and an agent manifest/card for discovery (A2A’s agent card vs. ACP’s agent manifest, both JSON descriptors of capabilities). Thus, conceptually, they align on what needs to be communicated (tasks, capabilities, updates), even if the exact implementation (JSON-RPC vs REST endpoints) differs slightly.

• Supported Agent Types and Use of LLMs: Both protocols are agnostic to the type of agent – an agent could be an LLM-based reasoning agent, a tool-augmented agent, a symbolic AI, etc. They are meant to enable any agent to talk to any other. A subtle difference is philosophical: A2A draws a clear line between tools vs agents, saying that agents use tools (via something like MCP) but agents communicate via A2A. ACP blurs that initially by building on MCP – so an ACP agent might treat another agent somewhat like a “tool/resource” at first. But as ACP diverges, it too emphasizes agents as primary actors, similar to A2A’s worldview. Neither protocol restricts whether an agent uses an LLM or not, but both are clearly born out of the LLM agent paradigm (handling natural language and long-horizon tasks). They each support multi-modal content and long-running interactions.

• Integration Ecosystem: A2A launched with extensive industry backing – multiple partners and integration into Google’s Vertex AI ecosystem (via ADK). This means from day one, many vendors are exploring adding A2A support, and Google Cloud customers have tools (ADK, etc.) to use A2A.
  ACP, on the other hand, is integrated into IBM’s BeeAI (an experimental platform) and provided to the open-source community via SDKs, but doesn’t have a huge list of vendors announcing support yet (it’s more grassroots). Over time, ACP could get rolled into IBM’s enterprise offerings, but that’s speculative. So A2A currently has an edge in wider adoption potential, while ACP has an edge in the hacker/developer community experimentation thanks to embracing open-source agent frameworks out of the box.

• Security: Both protocols acknowledge enterprise security needs, but A2A has a more clearly defined security model at launch (with authentication schemes, tokens, etc. similar to OpenAPI). A2A is “secure by default” and emphasizes trust, compliance, and even usage in air-gapped networks.
  ACP is still formulating its security best practices; in a contained BeeAI deployment, security is more about network isolation and trusting code you run. We can expect ACP to incorporate strong auth as it matures (especially as it separates from MCP’s context-sharing focus, it will need direct agent auth controls). Both will likely converge on similar approaches: secure transport (HTTPS), agent identity and credentials, and permission negotiation. At the moment, A2A provides more assurance for inter-company or cross-cloud security, whereas ACP’s local-first model provides security in the sense of data not leaving your system (privacy by locality).

• Privacy: Both allow private deployment. A2A, though open, can be entirely self-hosted (you can run agents in your own environment with A2A, not necessarily exposing them). ACP/BeeAI explicitly allows offline use. Privacy considerations are similar – avoid sending data to untrusted third parties by confining communications to known agents. IBM’s local emphasis might appeal to those who avoid any cloud. Google’s inclusion of big partners suggests A2A will be implemented in many SaaS offerings – users of those will rely on those vendors’ privacy practices.

• Scalability: Google’s A2A was built with cloud scale in mind – large enterprise estates of agents across different clouds. It’s intended to handle high loads and numerous concurrent interactions by scaling each agent service horizontally. IBM’s ACP in its current incarnation is more limited to single-host or single-coordinator scale (suitable for tens of agents, not necessarily hundreds spread across a data center unless you architect multiple BeeAI instances). So in the near term, A2A is the choice for massive, distributed multi-agent deployments, while ACP is the choice for a controlled multi-agent cluster or local environment. This could change as ACP evolves (IBM might enhance BeeAI to handle distributed setups). Performance-wise, both use lightweight JSON messages; neither has shown a clear performance disadvantage. If anything, the extra hop via BeeAI in ACP could introduce slight latency versus direct agent-to-agent in A2A, but within a LAN that’s minimal. Both protocols will need real benchmarking as they mature.

• State of Maturity: A2A is a beta/early release but backed by a polished spec and a coalition, implying it might stabilize faster. ACP is alpha/pre-standard, more experimental and rapidly changing. Early adopters of A2A might find better documentation and support (Google’s resources, partner help) while early adopters of ACP get more of a blank canvas (and direct interaction with IBM researchers on GitHub). Depending on an organization’s appetite, one might prefer A2A if they want something that seems more officially supported, or ACP if they want to actively shape the direction of the protocol.

• Community and Governance: A2A, while open source, is largely driven by Google (at least initially) with partner input. ACP is being developed openly with community input under IBM’s guidance. It’s possible these efforts could converge or at least learn from each other. It’s notable that both reference Anthropic’s MCP, and indeed Google calls A2A complementary to MCP, while IBM calls ACP an extension of MCP. This triangulation around MCP means all three could potentially interoperate: for example, an agent might use MCP to access a database, use A2A to talk to an agent outside, and use ACP to coordinate with a local agent group. There is already talk that ACP and A2A could be used together (BeeAI adopting A2A for external comm). So, similarity: both are part of a broader emerging ecosystem of agent protocols and not necessarily mutually exclusive.

• Differences in Emphasis: Google’s A2A emphasizes inter-framework operability and enterprise workflow integration (with lots of enterprise software partners in the mix). IBM’s ACP emphasizes open-source agent orchestration, discovery, and telemetry within a cohesive platform. A2A is immediately outward-looking (how Salesforce’s agent talks to ServiceNow’s agent, as a hypothetical), whereas ACP is inward-looking first (how to run a bunch of different agents within one solution).

In conclusion, Google A2A and IBM ACP share the common goal of enabling AI agents to work together seamlessly, reflecting parallel innovation paths. They differ in their approach: one is an industry-wide push from a cloud giant, the other a research-driven open experiment from a computing giant. A2A is more ready for cross-domain agent federation, while ACP focuses on simplifying multi-agent setups in one domain (for now). It will be interesting to watch how these protocols develop through 2025 and beyond – whether one becomes dominant, or if they carve out different niches (with A2A standardizing external agent comms and ACP being used for internal agent orchestration), or if convergence happens (perhaps via a standards body or cross-pollination of ideas).

For developers and organizations, the emergence of A2A and ACP is largely positive: it means you won’t have to reinvent the wheel to get your AI agents talking. Choosing between them might come down to immediate needs: if you need to integrate with many vendor solutions, A2A’s backing makes it attractive; if you are experimenting with open-source agents locally, ACP (BeeAI) provides a ready playground. In the long run, we may even see tools that support both A2A and ACP, given their shared foundations, allowing the best of both worlds – a sign that ultimately, the AI agent community is converging on the idea that interoperability is key, even if there are a few different roads being taken to get there.

Sources: The information in this report is based on the official Google Developers Blog announcement of A2A Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog, Google Cloud documents and third-party analyses of A2A Google Open-Sources Agent2Agent Protocol for Agentic Collaboration - InfoQ, as well as IBM Research’s blog on BeeAI and ACP Introducing multiagent BeeAI - IBM Research , IBM’s ACP draft documentation Introduction - BeeAI , and commentary from industry watchers comparing A2A and ACP MCP, ACP, A2A, Oh my! — WorkOS . These sources provide insight into the design, objectives, and reception of both protocols as of their April 2025 release.