If an agent only works on curated prompts, you do not have a system. You have a demo.
Question
What must be true before an AI agent is safe to ship to production?
Quick answer
Before release, verify five controls:
- input validation,
- bounded tool permissions,
- deterministic fallback behavior,
- human escalation path,
- traceable action logs.
Reliability gate
Ask this question:
Can the agent fail safely when context is incomplete, contradictory, or adversarial?
If answer is no, block deployment.
Minimal launch standard
- Define non-negotiable no-action conditions.
- Add retry limits and timeout ceilings.
- Require confidence thresholds for irreversible actions.
- Capture full reasoning artifacts for audit.
Without these, your incident timeline becomes guesswork.
5-minute launch rubric
| Gate | Pass signal | Block signal |
|---|---|---|
| Input control | Schema validation + reject list is active | Free-form input goes straight to tool calls |
| Action control | Tool scopes are least-privilege and explicit | Agent has broad unbounded permissions |
| Fallback behavior | Known-safe fallback path is documented and tested | Failure path is undefined or human-dependent |
| Auditability | Request, context, decision, and action logs correlate by ID | Logs are partial and cannot reconstruct incidents |
If any row is "Block signal", delay launch and fix that row first.
10-minute action step
- Choose one real workflow where this decision applies today.
- Define one pass/fail metric before you test (cost, latency, reliability, or risk).
- Run 10 realistic examples and log misses with root cause tags.
- Ship only the smallest fix that moves your chosen metric.
Success signal
You can show a before/after metric change with a written decision rule the team can reuse.
