The short answer
What buyers are actually shopping for
Most people who search for an AI agent development company are not looking for another chatbot tutorial. They have a workflow in mind: intake, research, operations, customer support, internal tools. They have seen a demo. They want a company that can ship something their team will actually use.
The failure mode I see is buying the demo. The vendor shows a clean run on a happy path, the contract starts, and six weeks later the agent still cannot be trusted with a live customer or a live record. The gap is usually not the model. It is ownership, evals and support.
If you need AI strategy leadership rather than a build, see the fractional Chief AI Officer service and What is a fractional Chief AI Officer?.
Production agents, not demos
A production agent does work in your systems. It reads your data, calls your tools, and has to be right often enough that a person can trust the output, or catch it when it is not.
That means four things have to be designed up front:
- The workflow, written down, including what the agent is not allowed to do.
- The tools and accounts it may use, with permissions that match that rule.
- Evals on real cases, not only the ones in the sales deck.
- A fallback: who reviews a low-confidence action, and how a bad one is rolled back.
If a company cannot talk about those four things without slipping back into model names, keep shopping.
How to evaluate a vendor
Ask these in the first conversation. They are more useful than a stack list.
- Who reviews agent output before it can act in production, and what is their seniority?
- Can I see the eval set you would use on my workflow, including the failing cases?
- How do those evals run after launch, and who reads the failures?
- Who is the named person who approves a change to production data or a customer-facing action?
- Is the repository, the prompts, the evals and the cloud account in my name from day one?
- What do you do when the agent is confidently wrong?
You should also ask how they build. An AI-first team can be the right shape for this work, but only if a senior engineer is still accountable. I compared that model with a traditional agency in AI-first build team vs traditional agency.
What a serious engagement looks like
The work starts with a paid discovery, so the workflow and the evals are written down before anyone builds. Discovery should leave you with a written scope: the job the agent will do, the systems it will touch, the evals that define "good enough," and the support model after launch. You then decide whether to build, and the build is scoped and quoted on a call.
After launch, you need someone who is still watching the evals. Agents drift. Tools change. A vendor who disappears after the demo week is leaving you with a system you cannot operate.
When we take this work, a vetted partner engineering team, run by a Kompella product lead, ships it. I am accountable for the architecture and for what we ship to production. Details of the build path are on Build & Ship. If you also want a CTO involved in the wider product, see What is a fractional CTO?.
Book a call if you have a workflow
If you are comparing AI agent development companies, book a call. Bring one workflow. We will tell you on the call whether it is ready for an agent, and what the eval set would have to prove.
