Live
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.
- R01Every finding is reproducible from a saved trace.
- R02Calibration is measured: does 0.9 confidence mean right nine times in ten?
- R03Every automated decision can be explained and contested, as regulators are starting to require.
- 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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