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Agent Reliability Audit

Before your agent touches customers or money: consistency, robustness, calibration and safety, measured.

The split

LLM writes
Generates adversarial variants and writes the report
System One decides
Grades each trace: correct, consistent, safe, calibrated
Code executes
Runs the harness, repeats, simulates outages, computes metrics

Engine

A harness modelled on Princeton's agent-reliability work: repeated runs, reworded prompts, simulated tool outages, prompt-injection suites, leak and destructive-action checks. The brain grades every trace.

Rulebook

What correct means here, and the mistakes we've already caught. It grows one job at a time.

  1. R01Every finding is reproducible from a saved trace.
  2. R02Calibration is measured: does 0.9 confidence mean right nine times in ten?
  3. R03Every automated decision can be explained and contested, as regulators are starting to require.
  4. R04Destructive and money-moving actions are tested in a sandbox only.

Intake

  • Sandbox access to the agent
  • The workflow and what can go wrong
  • Real or synthetic test cases
  • Rules that apply: NDPA and CBN circulars, Ghana's data protection drafts, the EU AI Act, FCA or RBI guidance
UNIT
One agent workflow audited
REVIEW LAYER
A senior engineer signs every report.
DELIVERY
Scores per dimension, failing traces, fixes, and a re-test after you fix them.
TURNAROUND
5–10 working days

How we find buyers

  • Fintechs facing the CBN's automated AML requirement
  • Startups selling agents to banks and telcos
  • Regulatory sandboxes and innovation hubs

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