How long does it take to build an AI agent?

"Build an AI agent" covers wildly different scopes, so the honest first answer is: it depends what you're actually building. An agent that reads incoming form submissions and drafts a categorized reply is a different project than an agent that prospects leads, checks a CRM, drafts personalized outreach, and follows up automatically over days. The second one isn't ten times harder to write — but it is many times harder to trust running on its own, and trust is where most of the time actually goes.

The AI reasoning part — the prompt, the model call — is usually the fastest piece to get working. What actually consumes the calendar is integration work: authenticating against each system the agent touches, handling that system's specific API quirks and rate limits, and deciding what the agent does when an API call fails, times out, or returns something unexpected. A CRM integration that looks like a one-line API call in a demo is rarely one line once you account for pagination, field mapping, and duplicate records.

The second big time sink is testing on real data, not sample data. A demo that works on three clean example leads tells you almost nothing about how the agent handles a lead with a missing phone number, a name in a different script, or an ambiguous request. Serious builds run the agent against a batch of real historical cases, review what it produced, and tighten the prompt or add a guardrail before it's allowed to act without a human checking its output first.

Agents also aren't a one-time build the way a static webpage is. Prompts drift in effectiveness as the underlying model updates, source systems change their API shape, and edge cases surface once real volume hits that never showed up in testing. Budget for a maintenance loop, not just a launch date — the agents that stay reliable are the ones someone keeps watching and tuning, not the ones shipped and forgotten.

The practical way to shorten the real timeline: scope one workflow narrowly, ship it, watch it run against real traffic for a week or two, then expand. Agents that try to do everything on day one take the longest to trust and the longest to actually go live.

One more variable worth naming honestly: the number of external systems involved doesn't add linearly to the timeline, it multiplies. Two integrations is manageable; five integrations means five different failure modes, five sets of credentials to manage, and five places where an upstream change can quietly break the agent months later — which is exactly why starting narrow and expanding gradually beats trying to wire everything up in one pass.