AI agent audit
An AI agent evaluation on your real tasks, and a pressure test of the whole system around the agent: retrieval, tool permissions, escalation and traceability. You get scores, every failure with its transcript, and a plan to close the gaps.
- Typical duration
- 1–2 weeks, then rerun on demand
- Who it’s for
- Teams whose agent works in demos and is heading to real users
- You leave with
- Scores on your real tasks, a ranked list of failure modes, and a plan to fix them in order.
What you receive
- An evaluation suite built from your real tasks, with scores for task success, grounding and policy adherence
- Findings from realistic and adversarial pressure tests, with transcripts and reproduction steps
- Gaps in retrieval, tool authorization, fallback and logging, ranked by severity
- A runnable benchmark your team keeps, and an engineering plan to close the gaps
How it runs
- Week 1Define
Real tasks, edge cases and what the agent must never do become a test suite, agreed with you before anything runs.
- Weeks 1–2Test
The agent runs the suite across models and releases, then faces realistic and adversarial scenarios against the complete system. Every reported failure is verified by an engineer.
- ReadoutReport
Where it succeeds, how it fails, ranked findings with reproduction steps, and the plan to close them.
Is it the right fit?
Best when
Your agent is approaching launch, already live, or about to change prompts, models or workflows, and it has become too important to the business to trust on intuition.
Questions from the first call
Related servicesAI products and agentsMVP stabilization
What is an AI agent audit?
An evaluation of an AI agent and the system around it. We score the agent on your real tasks, pressure-test retrieval, tool permissions, escalation and logging with realistic and adversarial scenarios, and report every failure with its transcript.
When should we audit an agent?
Before launch, before a change of model, prompts or workflow, or once the agent is live and too important to the business to trust on intuition.
What is AgentBenchtest?
The evaluation tooling we use to run and score suites repeatably. Every failure in the report is verified by an engineer.
Which agents can you audit?
Any agent we can call: hosted models via API, open-weight models in your cloud, agents built on frameworks such as LangGraph or LangChain, and agents on hosted or no-code platforms. Most checks concern behaviour and configuration rather than code.
Do you need production access?
No. Staging, or the ability to run the agent against test data, is enough. We never need write access.
Will you tell us to rebuild?
Only if the evidence says so, and the report shows that evidence. Most audits end in a remediation list.
Do you test for prompt injection and unsafe tool use?
Yes. The adversarial scenarios try to make the agent ignore its instructions, leak data or call tools it should not, alongside checks of tool authorization, escalation and fallback.
Can you fix what you find?
Yes, quoted separately from the audit so the findings stay independent of the fix.
Start with a conversation
We reply with questions or a time for a call, then send a written scope and quote.