AI Agent Integrations
What Clarivy gives an AI agent
| File | Purpose | Agent rule |
|---|---|---|
agent-bundle/manifest.json | Machine-readable inventory, action graph, evidence map, verification plan, adapter notes, memory delta, and handoff notes. | Use this as the entry point. |
ai-employee-brief.md | Structured Markdown with stable finding_id, evidence_id, and action_id. | Summarize by ID; do not paraphrase unsupported claims. |
raw-data-index.md | Index of every endpoint-query datapoint and linked raw JSON. | Verify evidence before recommending edits. |
| Raw JSON evidence | Prompt, endpoint, sampled_at, model metadata, response text, citations, and error state where applicable. | Treat current-run raw JSON as the evidence source. |
/llms.txt | Public facts about Clarivy's current product status, pricing, endpoint count, and limits. | Use it when describing Clarivy itself. |
/agent-feed/ | Machine-readable merchant profile, evidence policy, offer boundaries, and agent-safe comparison rules. | Use it before routing an agent to scope review or a commercial handoff. |
Enterprise Monitor agent package
Enterprise Monitor adds a stricter operational package for enterprise AI agent employees. These files are designed for planning, verification, and handoff; they do not remove human approval gates.
| File | Purpose | Approval boundary |
|---|---|---|
agent-action-backlog.json | Prioritized action records with owner hints, risk level, due window, finding IDs, and evidence IDs. | Actions can be drafted by an agent, but high-risk records keep approval_required: true. |
agent-bundle/evidence-map.json | Machine-readable mapping from findings and actions to raw evidence files. | Agents must verify current-run evidence before editing customer systems. |
agent-bundle/verification-plan.json | Checklist of raw JSON files and uncertainty flags to inspect before action. | Low-confidence or missing evidence must remain visible in handoff notes. |
agent-bundle/memory-delta.json | Change summary versus prior Clarivy runs for the same customer subject. | Memory is context only; current-run claims require current-run evidence. |
See the Enterprise Monitor sample package for a public mock of the monthly report, weekly pulse memo, agent manifest, and agent action backlog. When Monthly Improvement Plan is scoped, the same backlog is curated into owner-ready work packages for human owners and enterprise AI agent employees; it remains evidence-bound and does not guarantee rankings, traffic, revenue, or answer share.
Native ecosystem compatibility
These are compatibility notes for file-based agent workflows. They are not vendor certifications or marketplace approvals.
| Agent ecosystem | Status | Best-fit Clarivy handoff |
|---|---|---|
| Claude Code | File-compatible | Read the Markdown brief and manifest, then use raw-data-index links to verify evidence before editing content or docs. |
| Codex | File-compatible | Use stable action IDs as implementation tasks, commit scoped fixes, and cite raw JSON evidence in handoff notes. |
| OpenCode | Compatible pattern | Use the same manifest and Markdown brief workflow; adapter-specific examples still need manual verification. |
| WorkBuddy | Native ecosystem priority | Target workflow is business-ops task assignment from action_id plus evidence verification from raw-data-index. |
| Hermes | Native ecosystem priority | Target workflow is reading the manifest, extracting work packages, and preserving evidence links during handoff. |
| Pi and conversational assistants | Reader-compatible | Use the human report plus AI-readable brief for explanation and stakeholder summarization, not autonomous edits. |
| Chinese agent workflows | Reader-compatible | Use zh/llms.txt, Chinese FAQ/methodology summaries, and the same evidence files; English remains canonical for product facts. |
Agent workflow
- Load scope. Read the manifest first and identify audit subject, product tier, prompt set, and file inventory.
- Read findings. Use the Markdown brief to map findings to action IDs and evidence IDs.
- Verify evidence. Open the raw-data index and linked raw JSON before changing customer content.
- Plan work. Turn action IDs into scoped tasks; keep evidence IDs attached to each task.
- Respect privacy. Do not move contact details, billing data, raw responses, or raw JSON into long-lived memory unless the customer explicitly authorizes that storage.
- Report uncertainty. If an endpoint errored, a response is missing, or a finding is low confidence, preserve that status instead of smoothing it away.
Privacy boundary for agents
Repeat-audit memory is designed for continuity, not surveillance. It may include prior run summaries, prior GEO scores, recurring findings, previous actions, and open loops for the same customer subject. It excludes email, contact names, addresses, billing data, tax ids, raw response text, and raw JSON payloads. Customers can opt out or request deletion.
What agents must not infer
- Do not infer that Enterprise Monitor creates a subscription by default. It can be prepaid or subscription-based only after scope, cadence, payment structure, and cancellation terms are approved.
- Do not infer that Professional or Enterprise Audit has a public fixed price today.
- Do not infer guaranteed rankings, traffic, revenue, or AI answer share.
- Do not infer native certification by Claude Code, Codex, OpenCode, WorkBuddy, Hermes, Pi, or any Chinese agent vendor.
- Do not treat historical Day-1 self-audit numbers as the current Snapshot contract.
FAQ
Can AI agents consume Clarivy audit output directly?
Yes. Clarivy audits include an AI-readable Markdown brief, raw-data index, raw JSON evidence, and Agent Bundle Manifest. Agents should verify recommendations against raw JSON before taking action.
Does Clarivy store raw responses in repeat-audit memory?
No. Repeat-audit memory excludes raw response text, raw JSON payloads, contact details, billing data, tax ids, and addresses. It stores sanitized historical summaries for same-customer continuity only.