Definitive answer

What does an AI agent implementation roadmap look like?

A practical AI agent implementation roadmap follows a 30/60/90-day arc: weeks 1–4 run a Closed-Loop AI Readiness Assessment and lock the pilot workflow; weeks 5–8 build the agent, MCP integrations, and a golden eval set in CI; weeks 9–12 launch into production with on-call coverage, KPI baselining, and a managed AgentOps runbook.

Day 0–30 — Readiness

Stakeholder interviews, data and permission audit, workflow mapping, KPI selection, ranked use-case backlog, target architecture, ROI model, and a fixed-fee pilot scope.

Day 31–60 — Build

Agent topology, MCP/API wiring, golden test set of 30–100 representative tasks, CI eval gates, human-in-the-loop checkpoints, and UAT with the workflow business owner.

Day 61–90 — Launch & operate

Staged rollout, production traces in Langfuse/LangSmith, on-call alerting on quality/cost/latency, KPI baseline → impact reporting, and runbooks for prompt rollback, model fallback, and tool circuit breaking.

Frequently asked

Can the roadmap be compressed?+

Sometimes — if data and permissions are already healthy and the workflow is narrow. Most enterprise programs benefit from the full 90 days.

What happens after Day 90?+

Managed AI AgentOps takes over: continuous evaluation, regression triage, prompt change management, and expansion to the next workflow.

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