Your agent nailed every test case. You shipped it. Within 48 hours, users report hallucinated outputs, silently dropped tool calls, and responses that bear zero resemblance to what worked on your machine. You reload the same prompt locally. It works perfectly. Welcome to the most predictable failure mode in AI engineering: the dev-to-prod gap.
This is Crucible C01. We dissect the five failure modes that kill agents in production and give you the tools to catch them before your users do.
The Idea (60 Seconds)
Developers test agents in idealized conditions: deterministic inputs, warm context windows, generous API latency budgets, and sequential tool calls. Production exposes the opposite environment: cold starts strip context, rate limits compress timing, and parallel calls introduce race conditions. The agent that performed flawlessly at temperature 0 on a 2k-token context window collapses at temperature 0.7 on an 8k-token window.
The five failure modes are temperature drift and context window overflow first; silent API errors and prompt drift follow; race conditions complete the set. Each one has a detection pattern and a fix, and this article delivers both plus the CLI tool to automate the detection.
