An agent is a language model that can call your systems. AI agent development is therefore an integration discipline: the model stops producing text somebody reads and starts taking actions somebody has to live with.
Everything below follows from that one change.
What AI Agent Development Has to Get Right
Human approval on every write. An agent that drafts the email, updates the record or raises the credit note, then waits for a person to press the button, captures most of the value with a fraction of the risk. That is the default here.
Tools with narrow scope. Each tool does one thing, takes typed arguments and is separately testable, so its behaviour can be reasoned about on its own.
Failure paths, not just the happy one. What happens when the API is down, returns an undocumented field, succeeds slowly, or gets called twice for the same task. Retries, idempotency and dead letter handling are most of the work.
A log you can reconstruct. Which tools were called, in what order, with what arguments and what came back. When somebody asks why the agent did that, this is the honest answer, and it is the part most often skipped and missed hardest.
Where Agents Genuinely Beat a Script
When the sequence of steps is not known in advance. A refund that might need a policy lookup, or three, or an escalation, depending on what the first lookup returns.
Where the steps are fixed, a script is cheaper, faster, fully testable and it does not change its mind. We will tell you which of the two your task is during scoping, and building the script is work we are happy to take.
That judgement is most of the value in a first conversation, and it costs nothing to ask for it.
The Loop Has to Terminate
An agent that can call tools can call them forever, so hard limits on steps, wall-clock time and spend per task ship as standard, along with the alert that fires when one is hit.
Those ceilings are what make the running cost predictable enough to put in a budget. We size them at scoping, and show the cost at ten times your opening volume.
What You Own at the End
The tool definitions, the prompts, the limits, the evaluation set and the logs, in your repository from the first commit.
Good AI agent development leaves your team able to add the next tool without calling us, because the tool interface is documented and the tests come with it. Tell us the task, and how bad the worst outcome is if it runs unattended.
Related Services
Whether the agent calls the right tool is AI evaluation, and on this kind of system it is the only meaningful measure of quality.
Where the agent needs to answer from documents before acting, that is RAG development. Where the tools it calls have to be built, or the integrations do not exist yet, that is software solutions.