Day 6 Alware Observability, Ben Rombaut
AI Summary
Overview of Observability in AI Wear
- Introduction to Observability
- Definition of observability in software systems.
- Focus on operational observability and its challenges in AI.
- Observability in Traditional Software
- Traditional systems are easier to instrument for observability.
- Tools like assertions help to trace issues effectively.
- Example of log tracing to understand errors.
- Challenges with AI Wear
- Complexity of reasoning in AI systems complicates observability.
- Issues with tracing decisions made by AI agents.
- Cognitive Observability
- Introducing cognitive observability to address the limitations of operational observability.
- Focus on understanding AI’s reasoning processes, not just its outputs.
- Framework for reasoning path observability (Watson) aims to observe without interfering with agent behavior.
- Key Components of Cognitive Observability
- Output Integrity: Assess the reliability of AI outputs.
- Semantic Feedback: Analyze user interactions with AI outputs to gauge effectiveness.
- Reasoning Path: Track the logic behind agent decisions to clarify and improve system performance.
- Watson Framework for Reasoning Path Observability
- Surrogate agents mirror primary agents to provide insight into reasoning processes.
- Generates multiple reasoning paths to ensure robustness and reliability of conclusions.