newsRed Hat AITrust 88 · LabPublished 1mo agoLive · 1mo ago
Why good AI agents fail in production: The missing infrastructure layer
It was 6 AM when the first alert fired. Then the second. Then the third.The on-call engineer opened her laptop to find 3 unrelated failures from a single AI agent. The agent handled support tickets, processed billing adjustments, and answered customer questions. It ran on LangChain. It had passed every test in staging, and it worked—until it didn't.I've watched teams pour months into prompt engineering and model selection only to lose a weekend cleaning up an incident that had nothing to do with
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- PossiblePossibly related (embedding) · 55%Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study →
- PossiblePossibly related (embedding) · 54%langchain-ai/langchain →
- PossiblePossibly related (embedding) · 54%Agent-Field/SWE-AF →
- PossiblePossibly related (embedding) · 57%ahumblenerd/tour-of-agents →
- PossiblePossibly related (embedding) · 56%Failure as a Process: An Anatomy of CLI Coding Agent Trajectories →
- PossiblePossibly related (embedding) · 54%muxi-ai/muxi →
- PossiblePossibly related (embedding) · 59%Jwuthri/Tracely →
- PossiblePossibly related (embedding) · 61%Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry →
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paperToward Continuous Assurance for the Democratization of AI Agent Creation in IndustryrepoJwuthri/Tracely-airepoJwuthri/TracelypaperFailure as a Process: An Anatomy of CLI Coding Agent TrajectoriespaperReasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational studyrepolangchain-ai/langchainrepomuxi-ai/muxirepoAgent-Field/SWE-AFrepoahumblenerd/tour-of-agents