Guides
How to detect false completion in AI agents
A practical evidence model for distinguishing completed, pending, failed, and unverified agent actions without confusing missing telemetry with failure.
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Guides
Practical guides to understanding and operating AI agents.
Guides
AI agent retry loops: distinguish recovery from repetition
Use action identity, changing evidence, and terminal outcomes to separate useful retries from unresolved loops and misleading success claims.
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AI agent monitoring for startups: a first-week implementation plan
Start with one important action, verify that its evidence reaches your monitor, then add a small reviewed failure set and an actionable alert.
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AI agent tracing: connect execution steps to the action you need to verify
Understand what a trace records, how asynchronous actions complicate it, and what evidence an investigator needs before replaying a failure.
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Production agent observability: five checks before trusting the dashboard
Verify capture completeness, action identity, evaluation coverage, reviewer evidence and a working response path before treating a green dashboard as success.
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LLM monitoring: connect service health, output quality and business outcomes
Build separate views of runtime health, evaluation results and authoritative outcomes, then connect them during investigation.
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