The honest answer is somewhere between "a weekend hackathon" and "eight months and counting." Where you land depends far more on a handful of specific factors than on how sophisticated the AI itself needs to be. Here's what actually drives the timeline.
The factors that actually matter
How many systems the agent needs real access to
An agent that only reasons over text you hand it directly is fast to build. When an agent needs live, scoped, authenticated access to your CRM, your scheduling system, or your database, that integration work is consistently the real bottleneck, not the AI logic. In more than one engagement, the client's backend team's API access work took longer than the entire AI build.
How much evaluation and testing the use case demands
A low-stakes internal tool can ship with lighter testing. A customer-facing agent handling financial or health data needs a real evaluation harness, adversarial security testing, and often a human-in-the-loop review process before launch. That adds real weeks, and they aren't optional if you want a system you can trust.
Whether you're building single-agent or multi-agent
A single agent with a handful of tools is a meaningfully smaller build than a multi-agent system with an orchestrator coordinating several specialists. Don't default to multi-agent complexity without first testing whether a single agent is actually insufficient; adding unnecessary architectural complexity adds real timeline for no accuracy gain.
How much of your data needs cleanup first
If the agent needs to reason over your existing data (for RAG, for classification, for anything grounded in your business's actual information) and that data is fragmented or inconsistent across systems, data preparation can end up being a larger share of the timeline than the agent itself.
Rough ranges by scope
| Scope | What's typically included | Typical range |
|---|---|---|
| Narrow internal tool | Single agent, limited tool access, internal users, lower stakes | 3–6 weeks |
| Customer-facing agent | Real system integration, grounded responses, evaluation harness, human handoff logic | 6–10 weeks |
| Multi-agent or high-stakes system | Multiple specialist agents, orchestration, extensive testing, regulated data | 10–16 weeks |
These assume discovery has already happened and the architecture is settled. Add two to four weeks up front for a proper discovery phase if you're starting from an open-ended idea rather than a scoped plan.
What actually speeds things up
- A real discovery phase before committing to an architecture. Counterintuitively, spending two to three weeks upfront mapping constraints usually shortens the overall timeline, because it catches infeasibility and integration surprises before they're expensive to fix mid-build.
- Starting with a narrower, provable scope rather than trying to handle every edge case in version one. Proving the core mechanism works, then expanding, is faster and lower-risk than building for full scope from day one.
- Having clean API access to the systems the agent needs, ideally confirmed before the build starts, not discovered as a blocker partway through.
How we scope a real timeline
We give a specific range only after discovery, based on your actual systems and data, not a generic estimate. If you want a rough sense before that conversation, our project cost estimator includes a timeline range by project type as a starting point.