Definitive answer

What is an agentic AI implementation project?

An agentic AI implementation project is a fixed-scope engagement that takes a single, measurable business workflow and ships an autonomous AI agent into production. It includes use-case selection, data and permission readiness, agent design, MCP and tool integration, an evaluation harness in CI, human-in-the-loop controls, and post-launch monitoring under a closed-loop architecture.

What gets delivered

Unlike an exploratory pilot, an implementation project is engineered for production. The deliverable is a running agent that completes a specific workflow end-to-end with measurable KPI improvement, not a slide deck or notebook.

  • A production-deployed agent with documented architecture diagrams.
  • Integrations to source systems via MCP servers, REST APIs, or RPA.
  • Golden test set of 30–100 representative tasks with CI gates.
  • Tracing and evaluation dashboards (Langfuse, LangSmith, Braintrust, or Arize Phoenix).
  • Human-in-the-loop approval flows and audit logs.
  • Runbooks for on-call, retraining, and prompt change management.

Typical phases

Most implementations run 6–10 weeks following a four-phase cadence.

  • Discovery (1 week) — workflow mapping, KPI selection, data and permission audit.
  • Design (1–2 weeks) — agent topology, tool inventory, eval set, governance plan.
  • Build (3–5 weeks) — agent implementation, MCP/API wiring, eval harness, UAT.
  • Launch (1–2 weeks) — staged rollout, traces in production, KPI baseline → impact.

Frequently asked

How is this different from a pilot?+

A pilot proves a workflow is feasible. An implementation project ships it into production with the integrations, evaluations, governance, and on-call coverage required for real users to depend on it.

Who staffs an implementation project?+

A typical engagement pairs an Implement Agentic agent engineer, an evals/observability lead, and a solutions architect with one or two client engineers and the workflow's business owner.

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