Blog

Insights on AI agent debugging, decision graphs, and building reliable autonomous systems.

How to Review Production Agent Failures Without Guessing

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.

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How to Debug Tool Calling Failures in AI Agents

A practical workflow for finding wrong tool choices, malformed arguments, missing results, repeated calls, and silent side effects.

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Replay Is the Missing Primitive for Agent Debugging

Multi step agent failures are hard to reproduce from logs. Replay gives developers the missing debugging primitive.

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A Developer's Checklist for Shipping Reliable AI Agents

Reliability is not one prompt change. It is a set of engineering checks around decisions, tools, traces, replay, and release gates.

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How to Investigate an AI Agent Failure Step by Step

A practical workflow for debugging agent failures with traces, replay, and decision graphs instead of guessing from logs.

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Why Agent Debugging Needs Decision Graphs, Not Just Timelines

Timelines show order. Decision graphs show causality. Agent debugging needs both.

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Why Agent Failures Are Invisible (And How to Fix It)

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.

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5 Signs Your Agent Infrastructure Isn't Production-Ready

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.

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The Decision Graph: How AI Agents Actually Think

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.

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Your Observability Tool Can't Debug Agents

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.

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Your AI Agent Just Failed. You Won't Know for Weeks.

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.

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Why AI Agent Logs Aren't Enough: The Case for Structured Traces

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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