# Evidence-backed AI-agent audits.

> Run automatic site checks across client sites with ranked fixes and retests. AI agent task tests for client journeys are coming later.

- Canonical URL: https://aiscovery.io/use-cases/agencies
- Markdown version: https://aiscovery.io/use-cases/agencies.md
- Last updated: 2026-10-21

Today: repeatable site checks with evidence and fixes your engineering teams can verify. Coming later: AI agent task tests for client journeys.

- [Apply as an agency partner](https://aiscovery.io/partners)
- [Request a demo](https://aiscovery.io/demo)

For digital agencies, consultancies, technical SEO teams and web delivery partners.

## Turn a broad AI question into a controlled client engagement.

Each engagement starts with owned journeys, declared boundaries and evidence that both client and delivery teams can review.

- **Qualify:** Identify the client journey, business risk, environments and owners before testing.
- **Baseline:** Run the same versioned checks across public pages, and later the same key tasks with named AI agents.
- **Compare:** Coming later: separate website defects from agent, policy and platform failures.
- **Explain:** Connect every priority fix to the evidence we saw and the customer impact.
- **Handoff:** Give the implementation team the failed check and how we will check it again.
- **Retest:** Repeat the same task contract after fixes and compare outcome, speed and friction.

## Keep scope, evidence and remediation connected.

One evidence contract prevents audit observations from becoming subjective presentation claims.

1. **Client contract.** Persona, goal, test data, permissions, stop gate and expected result are agreed before the run.
2. **Reviewable evidence.** Step logs, step screenshots, timing and final-state checks explain each outcome.
3. **Verified retest.** The same scenario confirms whether remediation changed the result without adding new risk.

## Agency risk starts when a result cannot be defended.

- **Generic checklist:** Technical presence is reported without proving impact on a real client task.
- **Unclear attribution:** A model or runner failure is incorrectly presented as a website defect.
- **Unsafe scope:** The run reaches a production write action without a declared stop gate.
- **Unverifiable fix:** A recommendation has no reproduction path, owner-ready criteria or comparable retest.

## Measure delivery quality as well as agent completion.

The result must support client communication, engineering action and a defensible follow-up test.

- **Task completion:** Coming later: track checked outcomes by key task, agent and attempt.
- **Time and friction:** Coming later: record step timings and repeated attempts.
- **Evidence coverage:** Confirm that priority fixes have report-safe evidence and an independent check.
- **Baseline to retest:** Compare results against the same fixtures, environment and versioned task contract.

## Client authorization defines the testing boundary.

Our checker and AI agents stop before login, cart, checkout, payment and any form that sends data. Authenticated and staging tests are coming later.

## Record technology state without turning emerging standards into sales theater.

Reports separate established web foundations, optional agent-native interfaces and technologies that were not available or not tested.

- Page titles, headings, language and structured data syntax
- Structured data, canonical sources and machine-readable content
- Coming later: rendering, network behavior and layout shifts during task runs
- robots.txt rules for AI crawlers, sitemaps and llms.txt
- Declared WebMCP forms when present: detected, not run
- Evidence redaction, retention and responsible disclosure

## Package the work for client and implementation teams.

- Executive summary with explicit confidence and limitations
- Coming later: results per key task for each agent that ran, with step screenshots
- Critical and High fixes with the evidence behind them
- Prioritized remediation backlog with acceptance criteria
- Baseline-to-retest comparison for approved fixes

## Agency partnership questions

**Q: Can reports be white-labeled?**

A: White-label and export capabilities are not a public product promise. Pilot deliverables and presentation boundaries are agreed individually.

**Q: Can one method cover different client stacks?**

A: Yes. The task and evidence contracts stay consistent while fixtures, technologies and assertions remain domain-specific.

**Q: Does a failed agent mean the client site is broken?**

A: No. Platform failures are ours and never count against the client site. Separating website, agent and policy causes is coming later.

## Keep one client audit defensible.

Scope authorization, version scenarios, preserve report-safe evidence, assign the right cause and run a comparable retest.

[Read the agency audit guide](https://aiscovery.io/resources/ai-agent-website-audits-for-agencies) (Practical delivery guide)

## Bring one client journey that needs a defensible answer.

Apply with an owned website workflow, a technical reviewer and a clear baseline-to-retest goal.

- [Apply as an agency partner](https://aiscovery.io/partners)
- [Request a demo](https://aiscovery.io/demo)

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