Agentic AI Ops Readiness Kit (2026)

Use this kit to take one workflow from prototype to production safely.

1) Define the workflow wedge (scope discipline)
- Pick ONE workflow with clear success criteria (e.g., “refund requests under $100” or “IT password resets”).
- Write an explicit definition of “done”: resolution rate, time-to-resolution, escalation criteria.
- List the systems the agent can touch (CRM, ticketing, billing, email) and classify each as read vs write.

2) Build the evaluation suite before scaling traffic
- Collect 100–500 real examples from logs (top intents + edge cases).
- Create labels for: correct outcome, policy compliance, tone, and required escalations.
- Add automated checks: schema validity, forbidden actions, PII leakage patterns.
- Set a launch bar: e.g., ≥90% pass on top intents and 0 critical violations.

3) Tooling and permissions (capability security)
- Wrap every tool with least privilege; default to read-only.
- Add parameter constraints (amount limits, domain allowlists, required tags).
- Require explicit approval gates for high-risk actions (money movement, user access, external comms).
- Implement a kill switch per tool and per workflow.

4) Observability and audit trail (debuggable by design)
- Log: user input, retrieved doc IDs + timestamps, tool calls + params, tool outputs, model outputs.
- Ensure traces are searchable by user, ticket ID, and outcome.
- Add loop detection signals: repeated tool calls, step count thresholds, retry storms.

5) Cost controls (budget as an SLO)
- Define budgets per successful outcome (P50 and P95).
- Implement graceful degradation: smaller model, reduced context, fewer retrieval passes, or forced escalation.
- Track “tokens per successful outcome,” not tokens per request.
- Set alerts for spend anomalies (e.g., 2× weekly baseline).

6) Rollout plan (reduce blast radius)
- Start in shadow mode: agent proposes, human executes.
- Move to limited production: 5% traffic, then 25%, then 100% with metrics gates.
- Require post-launch review after 7 days: top failure modes, costs, and user feedback.

7) Incident response (when—not if—something breaks)
- Classify incidents: prompt injection, retrieval miss, tool outage, model regression, policy bypass.
- Define first actions: disable tool, tighten policy, switch model, force escalation.
- Write a 48-hour postmortem process; add the incident to the eval suite as a regression test.

8) Ongoing governance
- Version prompts, policies, and tool schemas.
- Re-run evals on any model version change.
- Review long-term memory retention and privacy rules quarterly.

If you can’t (a) constrain capabilities, (b) measure outcomes, and (c) reconstruct actions from logs, you’re not ready to ship an agent—only a demo.