Insights on AI agent debugging, decision graphs, and building reliable autonomous systems.
A post incident workflow for AI agents: collect the trace, replay the path, inspect decisions, compare tool state, record root cause, and add a regression.
Read article →A practical workflow for finding wrong tool choices, malformed arguments, missing results, repeated calls, and silent side effects.
Read article →Multi step agent failures are hard to reproduce from logs. Replay gives developers the missing debugging primitive.
Read article →Reliability is not one prompt change. It is a set of engineering checks around decisions, tools, traces, replay, and release gates.
Read article →A practical workflow for debugging agent failures with traces, replay, and decision graphs instead of guessing from logs.
Read article →Timelines show order. Decision graphs show causality. Agent debugging needs both.
Read article →Your agent completed successfully. It also made the wrong decision. Zero errors, 100% completion rate — and 23 wrong refunds. Here's why invisible failures are the biggest risk in production AI agents.
Read article →Most teams ship AI agents with the same infrastructure they use for simple API calls. Here are 5 warning signs your setup won't survive production — and what to do about each one.
Read article →Agents think in graphs, not lines. See why linear traces hide the real story and how decision graphs reveal the true reasoning path behind every agent action.
Read article →Traditional LLM observability tools were designed for simple prompt-response flows. But agents are multi-step decision systems. Here's what real agent debugging looks like.
Read article →AI agents fail silently — returning 200 OK while making wrong decisions. Learn why current observability tools miss these failures and what real debugging infrastructure looks like.
Read article →Your logs show what happened. Structured traces show why — decision by decision, step by step. Learn why agent debugging requires more than flat API call logs.
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