How long does it take to ship an AI agent to production?

Last updated: June 2026

The honest answer is that it depends on five variables, and the five variables are knowable in the first week. A focused production MVP ships in six to ten weeks. A multi-team enterprise rollout runs three to six months on top of that. Most pilots that drag past twelve weeks never graduate to production at all. This post breaks down the timeline by phase, names the five scope variables that move it, and walks through the actual delivery shape of three production agents we shipped. It's a cluster child of our pillar on agentic AI workflow services, and it picks up where the failure-mode analysis in our production-failures post explained what stops the pilot from reaching day two.

Key takeaways

• A production MVP ships in 6-10 weeks for a focused scope: one workflow, one or two integrations, one user role. Multi-team enterprise rollout adds three to six months for evaluation, observability, access control, and capability transfer.
• 95% of GenAI pilots never reach production (widely cited MIT NANDA estimate, 2025). The reason is rarely the model. It is integration debt, governance gaps, and a 12-week pilot window that closed without a path to production.
• The 12-week graduation rule. Pilots that pass week 12 without a concrete production handover plan tend not to graduate. The number is empirical across enterprise deployments — beyond that window, the cost-of-attention math turns against the project.
• Five variables move the timeline. Integration count, data readiness, governance burden, evaluation depth, and team familiarity. Everything else is downstream of these five.
• The build phase is rarely what slows you down. It is Phase 2 — data and integration readiness — that runs over budget on most projects.

The five variables that move the timeline

Every CTO asking "how long" is actually asking five sub-questions. Naming them up front turns a vague estimate into a scope conversation.

Integration count. Each external system the agent reads from or writes back to adds days, sometimes weeks. A single-integration agent (read a knowledge base, return an answer) ships fastest. An agent that calls into a CRM, a finance system, an inventory database, and a customer email service costs four integration cycles. Most agentic workflows we ship at production sit at two to four integrations.

Data readiness. Is the data the agent needs already in a form it can read? Clean entity resolution, retrieval-friendly storage, agent-readable schemas — these are the data-platform work covered in the data foundations of agentic AI workflow services. Where the data is not ready, Phase 2 (below) grows by weeks. Most timeline overruns we see come from this variable, not from the build itself.

Governance burden. A consumer agent and a regulated-industry agent have wildly different governance budgets. Tool allowlists, output schemas, audit logs, agent registry, EU AI Act mapping — the five engineering surfaces in our governance reference all take engineering time. A high-risk agent under EU AI Act obligations adds two to four weeks of governance work that a low-risk internal agent skips.

Evaluation depth. A workflow where wrong answers cost the business money needs golden datasets, online LLM-as-a-judge evaluators, and a human-review queue, all built before launch. A lower-stakes workflow ships with lighter eval and matures it in production. The choice is a deliberate trade-off, not an afterthought.

Team familiarity. Is the in-house team that will eventually own the agent already familiar with the patterns, or is this their first agentic system? First-time teams need more pair-working time during the build, which slows the partner's pace but accelerates the eventual transfer. Returning teams ship faster.

Phase 1: Discovery and architecture (1-2 weeks)

A senior agent engineer and a senior data engineer scope the use case, the integrations, the data sources, the governance obligations, and the in-house team that will eventually run the system. The output is a one-page architecture diagram, a named risk list, and a delivery plan with concrete weekly milestones. The buyer always talks to the engineer who will lead the work, not a sales person.

The scope conversation answers the five variables. If any answer is unresolvable in week one, that itself is a finding — the use case is not validated enough for an MVP engagement and the right next step is a discovery sprint rather than a build.

Phase 2: Production MVP (6-10 weeks)

The team ships the agent into production for a focused scope. Narrow use case, one or two integrations, one user role. Multiagent architecture if the scope calls for it, single-agent if it doesn't. Evaluation, observability, and the human-review surface ship in this phase, not after — they are how we know the agent works.

A six-week MVP is realistic when integrations are one or two, data is reasonably ready, and governance burden is moderate. A ten-week MVP is the upper bound when integrations are three or four, or when governance is heavy (regulated industry, EU AI Act high-risk). Pushing past ten weeks usually means a variable was mis-scoped in Phase 1, and the right response is a checkpoint conversation rather than a quiet slip.

The MVP is in production — real users, real traffic, real consequences for wrong answers — at the end of this phase. Not a sandbox demo. Not a staging environment. Production.

Phase 3: Scale and capability transfer (3-6 months)

Multi-team rollout, additional user roles, additional integrations, and continuous capability transfer to the in-house team. Pair working, documented decisions, code reviews of the in-house team's first production changes. Governance hardens against the obligations the use case is subject to (SOC 2, EU AI Act high-risk obligations from August 2026, sector-specific).

This phase is also when the eval pipelines accumulate enough production data to be genuinely informative. The golden dataset grows. The LLM-as-a-judge calibration tightens. The human-review queue surfaces patterns that feed the next sprint of improvements.

Three to six months is the typical band. Heavier rollouts — multi-region, multi-business-unit, multi-language — extend toward six months. Tighter rollouts complete in three.

Phase 4: Hardening and ongoing

The exit signal is the in-house team shipping a production change without the partner's involvement. From that point the system is operated and extended by the buyer's team, with the partner available for the next adjacent system if and when the buyer calls. Hardening — performance optimisation, cost optimisation, governance evolution as regulations change — continues indefinitely, owned by the in-house team.

Three real timelines

The phases above are not theoretical. Three production agents we shipped:

The Aralab invoice automation agent is a Claude Sonnet 4.5 agent reading manufacturing invoices and writing back to GCP, Firebase, and Aralab's manufacturing finance system. Three integrations, moderate governance, returning team. Phase 1 took ten days. Phase 2 (MVP into production with Aralab's finance team) ran nine weeks. Phase 3 (scale + capability transfer to Aralab's engineering team) ran four months. Total from kickoff to handover: roughly seven months. The agent has been live for over a year and Aralab's team has shipped multiple production extensions without us.

The PepTalk agentic operations system is a multiagent architecture on AWS Bedrock with pgvector retrieval and Argilla for evaluation. The scope was wider — multi-agent orchestration across the speaker-engagement workflow — and that mattered. Phase 1 took two weeks. Phase 2 ran ten weeks (the upper bound, because the multi-agent eval pipeline was non-trivial). Phase 3 ran five months. The system is now operated by PepTalk with us alongside on adjacent work.

The SANA Hotels AI training platform used AI avatars and Anthropic Claude to personalise training delivery. Two integrations, moderate governance, first-time team. Phase 1 took ten days. Phase 2 ran eight weeks. Phase 3 ran three months. A second engagement for staff optimisation followed and shipped faster because the team was now familiar.

What makes a timeline slip

Three patterns account for most slips.

Data is less ready than the buyer thinks. This is the dominant cause. Phase 1 should surface it and either replan Phase 2 around data-foundation work or sequence the engagement so the data work runs in parallel. Slips happen when Phase 1 was too short to catch the gap.

Governance scope expanded mid-build. A change in the use case (now the agent touches customer PII, now it makes recommendations that affect credit, now it is in scope for the EU AI Act) adds engineering weeks that were not budgeted. The fix is a checkpoint conversation with explicit scope amendment, not a quiet timeline drift.

The in-house team has less bandwidth than promised. Capability transfer requires the in-house team's attention. When the team is also running production for the existing systems, the transfer slows. The fix is honest bandwidth planning in Phase 1.

The budget is the lock, not the timeline

The CTO's question "how long does it take?" usually has a second question behind it: how much will it cost? In agentic AI delivery, those two are tightly coupled — engineering weeks are the largest cost line — and the 12-week graduation rule applies to budget too. Pilots that drag past twelve weeks without a production path tend to consume the entire pilot budget and produce nothing operationally useful.

The shape of the conversation that works is the inverse. Lock the budget. Pick a scope that fits inside the six-to-ten-week MVP band. Ship into production. Then decide whether to extend. The phases above are designed for this conversation, and the agents we have shipped this way are the proof that the shape works.