AgentOps Leadership Checklist (30-60-90 Day Plan)

Goal: Deploy AI assistants/agents in a way that improves throughput WITHOUT degrading quality, security, or accountability.

30 DAYS: Establish control and visibility (baseline)
1) Approved tools list
- Pick 1–2 sanctioned coding assistants (e.g., IDE-based) and 1 sanctioned chat workspace.
- Enforce SSO/SCIM where available; disable personal accounts for work data.

2) Data rules (one page)
- Define what must NEVER be pasted into prompts (secrets, private keys, customer PII, credentials).
- Define what is allowed (public docs, internal docs with permissioned access, synthetic examples).

3) Workflow scope
- Choose 2–3 workflows to instrument first (e.g., PR drafting, incident summaries, support reply drafts).
- Write a simple rule: “AI can propose; a named human owns outcomes.”

4) Logging & audit baseline
- Start capturing metadata: user/team, tool, model/version, repository/workflow, timestamp, cost/usage counters.
- Decide retention policy (e.g., 30–90 days for raw logs; longer for aggregated metrics).

60 DAYS: Add guardrails and measurement (make it reliable)
5) Introduce an AI RACI
- For each workflow, assign Responsible (AI/tool), Accountable (human owner), Consulted, Informed.
- Publish in the team handbook.

6) Implement gates for high-risk actions
- Require human approval for: external customer communication, auth/billing changes, security-sensitive diffs, data exports.
- Add secret scanning and policy checks to CI for AI-assisted PRs.

7) Evaluation harness (small but real)
- Build a gold dataset: 50–200 real examples per workflow.
- Score outputs for accuracy, completeness, policy adherence, and citation/source correctness (if using RAG).

8) Metrics dashboard v1
- Adoption: AI-assisted PR share; % tickets drafted with AI.
- Quality: rollback share, defect rate, re-open rate for tickets.
- Efficiency: cycle time, MTTR, support handle time.
- Cost: AI spend per resolved unit (ticket, PR, incident).

90 DAYS: Scale safely (make it compounding)
9) Expand to tool-using agents carefully
- Start with read-only agents (summarize, classify, propose) before write-capable agents (create PRs, file tickets).
- Use least privilege: constrain tool access to specific APIs and scopes.

10) “Agent incident” process
- Any harmful or high-severity failure triggers a postmortem within 5 business days.
- Track root cause categories: bad retrieval, permissions, prompt weakness, ambiguous policy, missing tests.

11) Cost governance
- Set budgets per workflow (not just per team).
- Add alerts for spend spikes and latency degradations.

12) Review standards that scale with risk
- Define review checklists for generated code: tests required, lint/static analysis, security review triggers.
- Track whether AI increases senior-engineer review load; fix via better constraints and templates.

Success criteria after 90 days:
- You can quantify AI’s impact on at least two outcome metrics (e.g., MTTR down 15%, support deflection up 8%).
- AI-assisted changes do NOT have higher rollback/defect rates than baseline.
- Security can demonstrate controls: approved tools, auditability, retention settings, and prompt/data policies.
- Leaders can answer: “Where is AI used, what does it touch, how do we know it’s working, and who owns failures?”