Software Factory
Non-interactive software development where autonomous AI agents write code, run tests, and converge without human review—driven by specifications and scenarios rather than human implementation
Source: StrongDM AI (founded July 14, 2025)
Core Philosophy
A software factory is an enterprise-scale implementation of agentic development—moving from human-driven coding to autonomous AI systems that plan, execute, and deploy software with minimal human intervention.
Three Core Principles:
- “Why am I doing this?” (implied: the model should be doing this instead)
- Code must not be written by humans
- Code must not be reviewed by humans
Practical metric: If you haven’t spent at least $1,000 on tokens today per human engineer, your software factory has room for improvement.
What Changed: The Agentic Inflection (Late 2024)
Before: Compounding Error
Prior to October 2024, applying LLMs iteratively to coding tasks accumulated errors:
- Misunderstandings and hallucinations
- Syntax errors and version conflicts
- Library incompatibility
- DRY violations
- Result: “Death by a thousand cuts”—system decay and collapse
After: Compounding Correctness (Claude 3.5 Sonnet v2)
With Claude 3.5 v2 (October 2024) + Cursor YOLO mode, agentic coding began to compound correctness rather than error.
The breakthrough: Long-horizon agentic coding workflows that continuously improve their own output, similar to how correct feedback loops strengthen systems instead of degrading them.
Key Concepts
Non-Interactive Development (“Grown Software”)
Traditional: Specs → Humans write code → Code review → Deployment
Software Factory: Specs → Agents write code → Agents run harnesses → Agents converge → Deployment (no human review)
Agents work autonomously across entire software development lifecycle:
- Break requirements into subtasks
- Write complete implementations
- Run and debug tests
- Refactor for consistency
- Deploy to production
Humans define what (specifications), agents define how (implementation).
Scenarios (Not Tests)
Problem with tests: Tests are too rigid and can be reward-hacked by models
Solution: Scenarios
- End-to-end “user stories” (like holdout sets in model training)
- Stored outside codebase for independence
- Intuitively understood and flexibly validated by LLMs
- Represent real-world usage patterns
Key difference:
- Tests: Boolean (pass/fail), can be trivially manipulated
- Scenarios: Probabilistic, represent actual user satisfaction
Satisfaction (Not Pass/Fail)
Traditional: “Is the test suite green?” (Boolean)
Software Factory: “Of all observed trajectories through all scenarios, what fraction satisfy the user?” (Probabilistic)
Shift from boolean validation to empirical, probabilistic satisfaction metrics that reflect real-world effectiveness.
Architecture & Components
1. Agent-Driven SDLC
The entire software development lifecycle is automated:
Phase 1 - Make Knowledge Computable
- Consolidate requirements, constraints, decisions in queryable knowledge base
- Requirements assistant answers questions, highlights contradictions
- Proposes acceptance tests tracing back to stated needs
Phase 2 - Specification & Design
- Agents translate requirements into technical architecture
- Break complex features into implementation steps
- Design database schemas, API contracts, system boundaries
Phase 3 - Implementation
- Agents write complete, multi-file implementations
- Apply style guides and security best practices consistently
- Refactor automatically for code quality
Phase 4 - Testing & Validation
- Generate unit, integration, property-based, and adversarial tests
- Run continuous testing (not late-stage gate)
- Enforce security rules and policies as code
- Validate against scenarios, not just unit tests
Phase 5 - Deployment & Monitoring
- Automated deployment with standardized canaries
- Automatic rollbacks on failure detection
- Post-incident learning loops
- Continuous monitoring and optimization
2. Digital Twin Universe (DTU)
Problem: Testing against real third-party services is risky, limited, and expensive
- Rate limits
- Abuse detection
- API costs
- Can’t test dangerous failure modes
Solution: Behavioral clones of third-party services
DTU Includes:
- Okta (authentication)
- Jira (issue tracking)
- Slack (communication)
- Google Docs (document collaboration)
- Google Drive (file storage)
- Google Sheets (data)
Advantages:
- Validate at volumes far exceeding production limits
- Test failure modes impossible against live services
- Run thousands of scenarios per hour
- No rate limiting, abuse detection, or API costs
- High-fidelity behavioral replication (APIs, edge cases, observable behaviors)
This was economically infeasible before agents—now routine.
3. Agent Ecosystem Management
Agentic AI Studio: Curation and security of domain-trained agents
- Curate specialized agents by domain
- Set permissions and data scopes
- Define where human approval is mandatory
- Monitor agent performance and behavior
Agentic AI Mesh: Orchestrates handoffs across development lifecycle
- Requirements → Planning → Design → Coding → Testing → Operations
- Multiple specialized agents working in concert
- Start with supervised autonomy
- Expand privileges as reliability evidence accumulates
Economics & Productivity
Shift in Economics
Pre-agent era:
- Building a high-fidelity clone of SaaS application was technically possible but economically infeasible
- Teams self-censored proposals (“manager would say no”)
- Digital Twin Universe was unthinkable
Post-agent era:
- DTU is routine
- Deliberate naivete: Remove Software 1.0 habits and constraints
- What was unthinkable 6 months ago is now standard
Productivity Multipliers
- GitHub Copilot (code suggestion): 1.5-2x
- Agentic systems (autonomous workflow): 3-5x or higher
Agents don’t suggest improvements—they design, implement, test, and deploy complete features autonomously.
Cost Structure
Token spending as efficiency metric:
- Pre-factory: ~$100-500/engineer/day in compute
- Software factory: $1,000+/engineer/day in tokens
Higher token spend = More agent autonomy = Better compounding correctness
Human Role Evolution
From Coding to Orchestration
Before: Developers write code, manage implementation details
After: Developers define goals, orchestrate agent systems, make strategic decisions
Preserved Human Responsibility:
- Define specifications and requirements
- Validate scenarios reflect user needs
- Strategic architecture decisions
- Governance and oversight
- Human approval gates where required
- Audit trails and accountability
Shift in Value: Engineers move from routine task execution to complex problem-solving and strategic architecture.
Comparison with Traditional Approaches
| Aspect | Traditional | Software Factory |
|---|---|---|
| Coding | Humans write code | Agents write code |
| Code review | Humans review | Agents review themselves |
| Testing | Late-stage gate | Continuous, built-in |
| Validation | Unit tests (boolean) | Scenarios (probabilistic) |
| Refactoring | Manual | Autonomous |
| Deployment | Manual/scripted | Automatic with rollback |
| Scaling | Linear with headcount | Multiplicative with agents |
| Cycle time | Weeks/months | Days/hours |
Competitors & Similar Approaches
Others building software factories:
- Devin (AI software engineer)
- 8090 (agentic development)
- Factory (by Matan Grinberg & Eno Reyes)
- Superconductor (agentic engineering)
- Superpowers (by Jesse Vincent)
Critical Success Factors
1. Model Quality
Requires Claude 3.5 Sonnet v2 or better—earlier models compound errors
2. YOLO-Mode Equivalent
Need short feedback loops where agents test their own work immediately
3. Scenario Design
Scenarios must reflect real user needs, not be gameable like unit tests
4. Digital Twin Infrastructure
Ability to test at scale without hitting production limits
5. Agent Governance
Clear policies on where human approval is required, structured autonomy expansion
Implications for the Industry
Software Development as Manufacturing
Historical: Software development was craft-like (humans as engineers)
Future: Software development becomes manufacturing (agents as workers, humans as engineers/architects)
This parallels historical factory transitions in agriculture, textiles, and manufacturing.
Scaling Dynamics
Traditional: 100 engineers → 100x productivity improvement
Factory: 100 engineers + agentic infrastructure → 500x productivity improvement (exponential scaling)
Constraint becomes specification quality and scenario curation, not implementation labor.
Requirements Engineering Becomes Critical
As implementation becomes cheap/automatic, specification quality becomes the bottleneck.
Governance & Safety
Deliberate human governance:
- Define mandatory human approval points
- Maintain audit trails of agent decisions
- Monitor for specification drift
- Incident playbooks for agent failure modes
- Supervised autonomy expanding as evidence accumulates
Not full autonomous deployment—controlled expansion with evidence.
Getting Started
Practical form (from StrongDM):
- Identify candidates – Features with clear specifications and testable scenarios
- Build scenarios – Define end-to-end user stories for validation
- Set up DTU – Mock third-party dependencies you’ll test against
- Define agent roles – Which agents handle requirements, design, coding, testing
- Start supervised – Agents generate artifacts with human approval gates
- Expand autonomy – As reliability evidence accumulates, reduce approval gates
- Monitor & iterate – Track token spend, satisfaction metrics, cycle time
Key Metrics
- Token spend per engineer per day (target: $1,000+)
- Satisfaction metric (% of scenarios satisfied, not % tests passing)
- Cycle time (days/hours vs weeks/months)
- Autonomous actions per feature (% of work done by agents)
- Compounding correctness (does system improve over iterations?)
Strategic Implications
The software factory represents a fundamental shift in software engineering economics:
- Labor constraints disappear (agents, not headcount, limit scale)
- Specification quality becomes differentiator
- Scenario curation becomes core competency
- Agentic AI orchestration becomes critical infrastructure
- Speed-to-market becomes competitive advantage
Organizations that master software factories will ship 5-10x faster than traditional competitors.
Related Concepts
- Agentic Development – Autonomous AI in SDLC
- Digital Twin Universe – Testing infrastructure for agents
- Context Graphs – How agents maintain knowledge and decision lineage
- Claude 3.5 Sonnet – The model that enabled compounding correctness
References
- StrongDM AI: “Software Factories and the Agentic Moment” (https://factory.strongdm.ai/)
- Luke PM: “The Software Factory”
- Sam Schillace: “I Have Seen the Compounding Teams”
- Dan Shapiro: “Five Levels from Spicy Autocomplete to the Software Factory”