Articles & Notes

Notes on building reliable AI agents, cloud reliability, cost optimization, and automated systems.

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Published 2026-08-02

Why My Production AI Agents Kept Breaking at 3 AM (And How We Fixed It)

A practical guide on why autonomous AI agents fail in real-world production systems and how we built SRE reliability patterns, guardrails, and cost-efficient memory architectures to keep them running smoothly.

#large-language-models#ai-agent#artificial-intelligence

Key Engineering Takeaways

Context Window Explosion

Unchecked agent memory was causing runaway latency and huge token costs. We fixed this with sliding-window memory and structured state checkpoints.

Tool Call Failures & Hallucinations

Agents would hallucinate API arguments and get stuck in infinite retry loops. We implemented strict Pydantic schemas and circuit breakers.

Automated Incident Triage

Integrated SRE health checks and ServiceNow automated alert routing to catch degraded agent sessions before users notice.

Slashing Operating Costs

Reduced LLM token waste and API costs by over 40% with intelligent caching and local fallback routing.

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