I'm tired of manually debugging traces
Summary
A developer builds a debugging tool for AI agents that compares replays against reference runs to identify where behavior first drifted, expressing frustration with manual trace debugging.
Similar Articles
I got tired of W&B and Langfuse for debugging agents, so I built my own tracer looking for feedback
Built a new tracer for debugging AI agents that auto-detects loops, logs sessions as readable timelines, and allows side-by-side comparison. Seeking feedback.
@benhylak: we built the first sane way to debug your agent locally. you can see your traces. codex/claude code can too. this lets …
A new open source tool enables local debugging of AI agents by viewing traces, allowing automated eval writing and testing with tools like codex and Claude code.
@RespanAI: AI observability platforms raised $1B+ to reinvent print debugging for the agent era. Reading traces manually is not a …
Respan introduces an AI observability platform that automatically catches issues in traces, aiming to replace manual debugging for agent-based workflows.
How do you actually debug your AI agents?
Developer shares struggles debugging AI agents in production, highlighting issues with hallucinations, regression from prompt changes, and high API costs, asking the community for strategies.
Quick question for anyone running AI agents in production
A question highlighting the lack of observability in AI agent memory layers, asking how teams debug incorrect retrievals without full traceability.