AI Agent Development Cost: Real Numbers and What Drives Them
Nobody can quote your project from a web page, but the ranges are not a mystery either, and the shape of the estimate is remarkably consistent across the builds we have done and reviewed.
The three numbers you need are build, run and maintain. Teams routinely negotiate hard on the first, worry about the second, and forget the third entirely — which is why so many agents are quietly broken eight months after launch.
Build cost, by scope#
| Scope | Typical range | What it includes |
|---|---|---|
| Internal assistant, 2–3 read-only tools | $8k–$20k | Loop, tools, retrieval, small eval set |
| Internal agent with write access | $20k–$45k | Above plus permissions, audit, approval gates |
| Customer-facing support agent | $35k–$90k | Above plus escalation, tone work, monitoring, load |
| Agent inside your product | $60k–$150k+ | Above plus UI, multi-tenancy, SLA, versioning |
| Proof of concept only | $5k–$12k | One path, no permissions, not shippable |
Where the hours actually go#
The distribution surprises people who expect the model to be the project. Across builds, roughly: integrations and the tool layer 30%, evaluation and iteration 20%, permissions, audit and security 15%, monitoring and operational tooling 10%, prompt and retrieval work 15%, and the agent loop itself around 10%. Prompt engineering is the smallest line on the sheet, which is exactly why a quote that consists mostly of prompt engineering is a warning sign.
If a proposal has no line for evaluation, you are buying a demo. The evaluation set is what turns a demo into something you can change without fear.
Run cost is usually smaller than feared#
For a typical support-style agent, a completed task costs somewhere between a few cents and a few tens of cents in model calls, depending on context size and how many steps it takes. At ten thousand tasks a month that is real money but rarely the dominant number next to the labour it displaced. It also falls quickly with the standard measures — trimming context, routing simple steps down a tier, caching a stable prefix — which together commonly halve the bill without touching output quality.
Maintenance is the line people forget#
- Model deprecations: re-qualify on a new version once or twice a year.
- API drift: the systems your tools call will change without asking you.
- Retrieval upkeep: documents change, and a stale index is worse than none.
- Evaluation growth: new failure categories arrive with new users.
- Ownership: someone must be on call when the agent does something odd.
Budget 15–25% of build cost per year. An agent is a production service, not a project that finishes.
How to get a quote you can compare#
Ask every supplier for the same five things and the numbers become comparable: the tool list with argument schemas; who builds the evaluation set and how many cases; which actions sit behind human approval; what monitoring is delivered; and what a maintenance retainer covers. Ranges that differ by a factor of three almost always turn out to be quoting different scopes — one includes permissions and evaluation, the other is a demo with a nice interface.
Frequently asked questions
Why do quotes differ so much for the same brief?
Because the brief is rarely as specific as it feels. A quote covering permissions, evaluation, monitoring and a maintenance path is a different product from one covering a working happy path. Compare the five items above rather than the headline number.
Can we start smaller than these ranges?
Yes. Pick one narrow task, two read-only tools and twenty evaluation cases; that is often $8k–$15k and tells you whether the larger build is worth funding. It also produces the tool layer and evaluation harness the bigger project would need anyway.
Is it cheaper to build in-house?
Cheaper in cash, more expensive in time, and only if someone senior owns it. The usual failure is a promising internal prototype that nobody has time to take through permissions, evaluation and monitoring — which is where most of the cost was hiding.
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