Outcome 01
A readiness score
The audit scores the parts that matter for agent use: interface, permissions, action loops, observability, recovery, and handoff to a human.
Evidence-backed tool audit
Audit a tool before your AI agents depend on it.
Get a clear, evidence-backed view of whether a tool is suitable for the AI-agent workflow you have in mind.
The audit reviews available evidence such as public docs, shared materials, sandbox access, logs, traces, schemas, repo snippets, and the workflow you want to support. Sensitive actions, production access, and customer data stay out of scope unless explicitly approved.
Outcome 02
Evidence, not opinion
The report cites what was reviewed: docs, API responses, CLI behavior, auth flows, receipts, retries, failure cases, and any workflow traces available.
Outcome 03
A decision note
You get a short recommendation: use it, use it with constraints, wait for fixes, or choose a different tool. If the evidence is weak, the report says that plainly.
What the audit delivers
The audit reviews available evidence against a defined AI-agent workflow. It gives you a practical recommendation, not a security, legal, or compliance certification.
Delivery boundaries
- Good fit: tool selection, provider comparison, API/CLI behavior, sample workflows, sandbox systems, public docs, private docs you are allowed to share, logs, schemas, and small repo excerpts.
- Needs human approval: production access, customer data, billing changes, new credentials, destructive tests, legal/security claims, or direct changes in a client system.
- Not offered: compliance certification, penetration testing, guaranteed business results, or unsupervised changes to sensitive infrastructure.
- Deliverable: a written report with evidence, gaps, constraints, and a recommendation for whether and how agents should use the tool. See the sample report. Implementation help is separate and requires additional scoping.
What gets reviewed
The audit is designed for teams choosing developer tools, SaaS workflows, internal platforms, APIs, CLIs, MCP servers, automation surfaces, and operations-heavy products for agent workflows.
Interface
Can an agent discover capabilities, call them without browser fragility, understand errors, and verify outcomes?
Control
Are permissions, approvals, credentials, rate limits, audit logs, rollback, and irreversible actions designed for delegation?
Reliability
Do actions expose idempotency, status checks, receipts, retries, partial failure handling, and recovery paths?
Workflow fit
Does the tool fit the task your agent should run, and are the constraints clear enough to operate safely?
Example findings
These are the kinds of issues to catch before an agent depends on a tool. People often work around them; agents usually fail or take unsafe shortcuts.
No action receipt
An API returns {"ok":true} after starting a long-running job, but exposes no job ID, status endpoint, final artifact URL, or cancellation path.
Credential scope too broad
The only practical token can read and mutate an entire workspace, so teams cannot delegate narrow work without accepting unnecessary blast radius.
Ambiguous recovery
Retries can duplicate side effects because the API lacks idempotency keys, duplicate detection, or a safe way to reconcile partial completion.
Ask about a tool audit
Tell us which tool or workflow you are considering, what your AI agents need to do, and what decision the audit needs to support. If the work is a poor fit for this kind of evidence review, we will say so before it goes further.