What Approval Processes Should AI Agents Follow?
Agents ship safely with tiered approvals—pre-approvals, HITL gates, validators, and post-action review—wired to clear roles and KPIs.
Executive Summary
Use three layers of approval—pre-approval, real-time control, and post-action review. Pre-approve policies, data scopes, and tools. Add HITL for high-risk or novel actions, backed by automated validators. Log every decision with traces and reason codes. Review weekly to convert overrides into improvements and reduce risk while maintaining velocity.
Core Approval Layers
Rollout Process (Wire Approvals Safely)
| Step | What to do | Output | Owner | Timeframe |
|---|---|---|---|---|
| 1 | Classify actions by risk and novelty; set escalation rules | Approval matrix | Product/Risk lead | 1–2 days |
| 2 | Define policies, allow/deny lists, and data scopes | Governed tool access | Security/Platform | 3–5 days |
| 3 | Stand up HITL queue and reviewer checklists | Consistent reviews | Ops/SMEs | 3–7 days |
| 4 | Instrument traces, reason codes, and audit trails | Evidence & learning | MLOps | 3–5 days |
| 5 | Pilot in sandbox with replay tests and kill switch | Safety baseline | QA/ML | 1–2 weeks |
| 6 | Go live with quotas, holdouts; run weekly triage | Controlled rollout | Owner on call | Ongoing |
Metrics & Benchmarks
| Metric | Formula | Target/Range | Stage | Notes |
|---|---|---|---|---|
| Decision success rate | Successful decisions ÷ total | 85–95% | Run | Define per use case |
| Human override rate | Overrides ÷ total | < 5% | Run | Spikes indicate trust gaps |
| Regression rate | New defects ÷ release | 0–1 | Improve | From replay suite |
| Cycle time | End − start per decision | ↓ 20–40% | Run | Balance with quality |
| Learning velocity | Accepted improvements ÷ month | 2–4 | Improve | From post-mortems |
Deeper Detail
Approval processes for AI agents prevent bad actions and turn human judgment into system learning. Start by pre-approving policies, tool permissions, and data scopes. Publish an approval matrix that maps risk and novelty to the right gate. Register tools with typed schemas so validators can block unsafe calls before execution.
At run time, route high-risk or first-of-kind actions through a HITL queue with reviewer checklists and acceptance criteria. Add automated validators for policy, PII/PHI, schema, and budget. After actions complete, keep auditable traces—inputs, tool calls, outputs, costs, and reviewer decisions. Require reason codes on overrides so disagreements become training data. Run weekly triage to refresh prompts, retrieval corpora, and policies. This balances safety with speed and builds trust through evidence.
TPG POV: We operationalize agent governance across marketing, RevOps, and CX—combining experimentation and controls so teams ship improvements faster with less risk.
Explore Related Guides
Frequently Asked Questions
The product or risk owner, with Security and Data stewards, signs off on policies, scopes, and tools.
For irreversible, customer-impacting, or first-time tool actions; also when confidence is low or costs exceed thresholds.
Decision, reason code, evidence snapshot, and any edits; the system should capture a full trace automatically.
Rotate reviewers, sample work, enforce checklists, and audit reviewer quality with spot checks.
Correlation ID, inputs/outputs, tools called, validator results, reviewer identity, timestamps, cost, and latency.