Radar / Ideas / Autopsy: every agent incident ships a…

Autopsy: every agent incident ships a root-cause report and a regression test

daily ideaambitiousJEV confidence 0.522026-09-30

OutcomeEvery agent failure produces a verifiable root-cause report plus a regression test within minutes — the same incident never recurs twice.

The problem

Agent incidents — wrong emails sent, data deleted, runaway spend — get Slack threads and vibes, not root causes. Teams can't faithfully replay agent sessions, so the state-of-ai-agent-incidents-2026 catalog shows the same failure modes repeating across companies. Debugging tools show traces; nobody turns a trace into a diagnosis and a prevention.

The idea

On any agent incident, Autopsy pulls the full session trace (via persistent session memory), runs an agentic root-cause analysis — hypothesize, verify each hypothesis against trace spans, propose the fix — and generates both a citation-linked report and a regression test plus a prevention policy, so the exact failure can't recur. Ships as a post-incident CLI first, then a CI integration.

Why now

Visa open-sourced VVAH this week — an agentic vulnerability discovery AND remediation loop, i.e. the analyze-and-fix pattern generalized. Context Mode shows full agent session state can be persisted and replayed. Respan Span-01 gives behavior anomaly signals to localize where a run went wrong instead of reading the whole trace.

What it combines

VVAH (agentic root-cause + remediation loop: the reasoning pattern) + Context Mode (session memory: faithful incident replay) + Respan Span-01 (behavior scoring: fault localization). The mix closes the loop that each part leaves open: replay gives the evidence, scoring finds the fault, the agentic loop turns it into a fix and a regression test.

MVP

CLI that takes a Langfuse/LangSmith trace ID and outputs a root-cause markdown with span citations plus a pytest regression stub. Deliberately skip: auto-generated prevention rules, CI integration, multi-trace correlation.

Distribution

Sell to platform/ML teams running production agents; B2B2C via observability platforms (Langfuse/LangSmith integrations) and incident-tool marketplaces (PagerDuty). Per-seat plus per-incident-analyzed pricing.

Why it wins

LangSmith and Langfuse show you what happened (traces); PagerDuty manages the human incident process. Neither generates causal hypotheses or the regression test. Autopsy is the only step that turns an incident into a prevention.

Risks

Root-cause quality depends on trace completeness; hallucinated causes erode trust fast. De-risk: every claim in the report must cite a trace span ID (machine-verifiable), and the UI leads with confidence + evidence links, never bare assertions.

Build it with

Visa open-sources VVAH: agentic vulnerability discovery and remediation harnessAgentic discovery-and-remediation loop as the root-cause reasoning pattern.
Context Mode: context-window optimization for AI coding agentsPersistent session memory enabling faithful incident replay.
Respan Span-01: behavior-scoring decision model for AI agent monitoringBehavior anomaly signals to localize where the run went wrong.

Repo to start from

agent-autopsy — trace-ingestion adapters + root-cause prompt pack + regression-test templates; the hosted incident history and prevention-rule sync are the paid layer.

Evidence

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