AI Agent Reliability Audit — Early Access

A short research audit for teams building or piloting AI agents. The goal is to identify recurring reliability risks, missing controls, and the highest-priority fixes without requesting sensitive production access.

What the audit evaluates

  • Tool-call failures, malformed outputs and weak result validation
  • Long-running stalls, checkpointing and resume behavior
  • Context and memory integrity
  • Rate limits, retry behavior and runaway cost risk
  • Observability, provenance and reproducibility
  • Permissions, irreversible actions and escalation controls

The output is a concise reliability scorecard with your highest-risk areas, missing controls, and a prioritized remediation list. This is not a security certification, compliance attestation, or production guarantee.

Who this research is for

This early validation is aimed at teams already evaluating, piloting, or operating AI agents and experiencing failed runs, human intervention, rate-limit cascades, memory/context problems, cost overruns, difficult debugging, or weak run-level visibility.

Privacy boundary

  • Do not submit passwords or API keys
  • Do not submit private customer data
  • Do not submit unrestricted production logs
  • Do not submit proprietary prompts or secrets
  • Use architecture and failure-pattern descriptions only

Early-stage research experiment. No payment is being collected.