AI Agent Pilot Acceptance Checklist
Define Approval and Verification Boundaries
Before an AI Agent connects to enterprise data, tools, or workflow systems, teams need a clear acceptance framework. This checklist defines the boundaries an agent must operate within — what it can access, what actions require human approval, what evidence must be produced, and what conditions trigger a rollback.
How to define human approval and acceptance boundaries for an AI Agent pilot?
Start with one business workflow and one data boundary. Define action tiers that separate read-only, suggestion, and execution capabilities. Establish specific human review checkpoints where agent output must be confirmed before proceeding. Require evidence for every agent action (logs, before/after snapshots, reviewer confirmation). Set clear rollback conditions that automatically suspend agent access when boundaries are exceeded. The pilot proves the agent operates within these boundaries, not that the agent is "smart."
Pilot Acceptance Criteria Checklist
Each criterion must be verified with documented evidence before the pilot is considered accepted.
1Data Scope Definition
- Which tables, views, or document sets can the agent access? (List specific names)
- Are row-level and column-level restrictions defined?
- Is PII/PHI/regulated data explicitly excluded from agent access?
- Is there a read-only database user or API key created specifically for the agent?
2Action Permission Tiers
Agent can only read from defined data scope. No modifications, no suggestions.
Agent proposes actions, changes, or analyses. Human must explicitly approve before any execution.
Agent can execute pre-approved actions only after human sign-off on a specific proposal.
Reserved for well-defined, low-risk, fully logged actions with automatic rollback triggers. Not recommended for initial pilots.
3Human Review Checkpoints
- After initial data connection: review what the agent discovered
- Before any agent-proposed action: review the proposal and approve or reject
- After agent action execution: review results and confirm correctness
- End of each pilot day: review audit log for unexpected access patterns
4Evidence Requirements
- Agent action log: timestamp, action type, data accessed, result, reviewer ID
- Before/after snapshots for any data modification actions
- Human approval records: who approved what, when, and with what rationale
- Quality validation results for agent output accuracy
5Rollback and Stop Conditions
- Agent attempts to access data outside defined scope
- Agent executes an action without prior human approval (in Tiers 0-2)
- Agent output quality falls below acceptance threshold for 3 consecutive reviews
- Cost or API usage exceeds the pilot budget limit
What This Checklist Does NOT Cover
- Not a compliance certification. This checklist validates pilot operational boundaries, not regulatory compliance (GDPR, SOC 2, HIPAA, etc.).
- Not a replacement for security audit. A separate security review, penetration test, and architecture audit should be conducted before any agent reaches production data.
- Not a guarantee of agent behavior. LLM-based agents are probabilistic. Acceptance criteria reduce risk but cannot eliminate unexpected outputs. Human review remains essential.
- Not a one-time checklist. Boundaries should be re-validated whenever the agent's scope, data access, or action tiers change.
Frequently Asked Questions
Which team should own the pilot acceptance review?
A cross-functional group with at least one business owner (understands the workflow), one data owner (understands the data scope and sensitivity), and one security representative (validates access controls). No single role should sign off alone.
What is the minimum viable pilot scope?
One business workflow, one data source, read-only or suggest-only action tier, two human review checkpoints, one evidence package. Do not attempt to validate multiple workflows in one pilot.
How does Surinch InchStack support AI Agent governance?
InchStack provides the control plane for agent permission boundaries, audit logs, human approval workflows, quality evidence collection, and delivery receipts. It integrates with agent frameworks but keeps the human reviewer as the final authority.
Can I reuse this checklist across multiple agents?
The framework is reusable, but each agent needs its own specific data scope, action tier definitions, and acceptance thresholds. Do not copy-paste without reviewing each boundary.
Ready to run a controlled AI Agent pilot?
Start with a defined scope, clear boundaries, and human review checkpoints.