The missing layer between AI agent observability and incident response.

Multi-agent AI systems fail 41–87% of the time (MAST benchmark, NeurIPS 2025). Most of those failures are silent — no stack traces, no 500 errors, dashboards showing green while the agent loops, corrupts state, drifts from its persona, or quietly produces wrong answers. Traditional observability captures the trace. Evaluation scores the output. Neither tells you what went wrong — or fixes it.

Pisama sits between them. 57 purpose-built detectors covering loop detection, state corruption, persona drift, coordination failures, specification mismatches, hallucination, prompt injection, and 50 more failure modes — running in a tiered escalation pipeline that resolves 90%+ of cases at $0 per trace.

On the TRAIL benchmark, frontier LLMs score 7–12% on multi-agent failure detection. Pisama scores 60.1% at zero marginal cost, with 100% precision. On the Who&When attribution benchmark, Pisama + a single Sonnet call beats every published baseline.                                   

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