Watson: Making LLM Agents Observable and Debuggable
A framework to see inside the black box of autonomous AI systems
Watson provides a cognitive observability framework that reveals the implicit reasoning of LLM-powered agents, making them more transparent and debuggable without compromising performance.
- Extracts reasoning patterns from LLM outputs without requiring explicit reasoning steps
- Creates reasoning graphs that visualize the agent's cognitive process
- Enables real-time monitoring and debugging of autonomous LLM agents
- Improves error detection and system reliability without added latency
For engineering teams, Watson addresses a critical challenge in deploying autonomous AI systems: maintaining visibility into how decisions are made while preserving performance benefits of fast-thinking LLMs.
Original Paper: Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents