understand-agent-flow

Debug tool selection in a multi-agent framework by inspecting canHandle() and score() implementations with logging examples. Trace maxByOrNull selection and score conventions using AgentFramework logs to diagnose why specific tools are chosen for user inputs.

AgentFramework orchestrates LLM responses by mapping the execution flow from user input to final output, including optional embedding and multi-agent graph delegation. It traces memory state and prompt construction throughout the step-by-step pipeline.

Profile LLM vs tool latency in Kotlin agent workflows by applying diagnostic strategies to measure per-step latency. Trace memory state, handle tool or LLM failures, and profile hotspots to identify performance bottlenecks.

Yes, you can trace multi-agent graph delegation using AgentFramework logs. The diagnostic process requires access to these logs alongside tool canHandle and score implementations to diagnose agent orchestration and response generation.

Inconsistent agent replies often stem from unstable tool selection or fluctuating memory state. Reproduce the issue by tracing the AgentFramework execution flow, inspecting prompt construction, and verifying tool scoring logic with provided Kotlin snippets.