Why AI Agents Fail Silently — And How to Fix It A technical deep-dive into the observability gap in multi-step LLM systems

Short summary

Multi-step LLM agents can hallucinate intermediate steps while reporting success, making failures invisible to existing observability tools. Ajah is an open-source observability gateway that detects hallucinations, contradictions, runaway loops, and security threats across agent sessions with per-step quality scoring, drift detection, and circuit breakers, deployable in 5 minutes.

• •Existing observability tools (Datadog, Sentry) fail for multi-step agents because they miss intermediate hallucinations that cascade into wrong outputs
• •Ajah detects hallucinations per step, narrative drift across steps, dead loops, and prompt injection with session-tree visualization and automatic circuit breaking
• •Open-source; deployable in 5 minutes with SDK support for Python and Node; detects cost-compounding loops before they happen

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