Your AI agent may be acting inside a workflow no one can prove.
ProofWarden reviews one bounded AI-agent workflow and maps whether it can produce reviewer-verifiable evidence.
Start with one workflow, one action, one boundary, and one practical evidence outcome.
Core reviewer question
Can we prove the action stayed inside the boundary we thought we set?
What did the agent do?
Who allowed it?
What boundary applied?
What source system captured the event?
Where does the raw evidence live?
What would an evidence reviewer ask first?
The evidence gap usually hides in the handoff.
AI-agent teams often know what the agent was supposed to do. They may not know whether they can prove what actually happened when the agent recommended, prepared, approved, escalated, executed, or recorded an action.
Six evidence families, one bounded action.
The review turns uncertainty into a practical evidence gap map by asking where proof exists, where it is weak, and what an evidence reviewer would need next.
Action boundary
- Can the action be described in one sentence?
- Where is the line between recommendation, approval, execution, and escalation?
Authority and approval
- Who had authority at the time?
- Was approval required, optional, missing, or bypassed?
Control proof mapping
- Which control, contract, audit requirement, or customer assurance promise applied?
- Can that wording be mapped to event fields?
Source capture
- Was the event captured close to the system of action?
- Are timestamp, actor, source system, approval state, and result recorded?
Evidence vault
- Where are prompts, logs, approvals, documents, and payloads stored?
- Can sensitive evidence stay in customer custody while still being reviewable?
Reviewer readiness
- Could a reviewer understand the event without a long internal explanation?
- Are exceptions visible and shareable without exposing the whole system?
Actions can drift without anyone changing the formal policy.
Teams improve prompts, add tools, change queues, relax approvals, or connect new systems. The original boundary still looks stable on paper, but the actual action may have moved.
Sales renewal drafting becomes customer commitment
Original boundary
The agent drafts renewal-response text for accounts below a defined value, with a human owner sending the final message.
Drift pressure
Discount language, CRM follow-ups, and minimal review can blur the line between draft and customer-facing commitment.
Insurance submission triage becomes workflow disposition
Original boundary
The agent extracts submission facts and suggests a routing category for human review.
Drift pressure
Queue priority, delayed handling, and chat-only exceptions can make the routing result more consequential than the original boundary implied.
Procurement assistant becomes approval proxy
Original boundary
The agent prepares vendor-risk summaries for a procurement manager.
Drift pressure
Pre-filled approval fields and missing-objection rules can weaken proof of source review and human approval.
Support refund agent expands beyond limit
Original boundary
The agent recommends refunds below a fixed threshold for routine support cases.
Drift pressure
Prompt-level threshold changes, regulated edge cases, or post-action review can leave the active boundary unclear.
A focused diagnostic, not a vague AI strategy exercise.
ProofWarden produces an Evidence Readiness Memo for the workflow reviewed, the bounded action, the review boundary, current evidence, weak proof, key reviewer questions, and one recommended next step.
Boundary statement
ProofWarden supports reviewability. Final judgment stays with the relevant reviewer.
Find out whether one AI-agent action can become reviewer-verifiable evidence.
Use the form to request a call. If there is a fit, ProofWarden will move into a structured intake for the selected workflow and bounded action.
For deeper preparation
The longer Evidence Readiness intake remains available for scoped review work after the initial conversation.
Open structured intake