How to build an AI agent
A practitioner walkthrough of building an AI agent — from defining the goal and wiring tools to adding memory, setting the authority bar, and evaluating before production.
Define a goal narrow enough to win
The most common reason agents fail is an ill-defined goal. "Handle customer support" is not a goal; "triage inbound tickets, answer the top twenty repeatable questions from our knowledge base, and route the rest to the right queue" is. A sharp goal tells you exactly which tools you need, what success looks like, and where the human boundary sits. Start narrow, prove it, then widen.
Write the success criteria before you write any code. If you cannot describe what a correct run looks like, you cannot evaluate the agent — and an agent you cannot evaluate cannot go to production.
Assemble the parts in order
- 01Pick and pin the reasoning model. Use a version-pinned model so behaviour does not drift between releases.
- 02Wire the tools. Give the agent scoped functions to read and act — a single integration first, behind a gateway that holds the credentials so the agent never does.
- 03Add memory. Short-term context for the current task; longer-term storage for what it should remember across runs.
- 04Build the loop. Observe, decide, act, repeat — with a clear stopping rule and a defined escalation path.
- 05Set the authority bar. Specify in writing what the agent may do alone, what it must confirm, and what it may never touch.
Evaluate, observe, and contain
Before launch, run the agent against a test set of real cases — including the awkward edge cases — and score it against your success criteria. After launch, instrument it so you can see every step it took: the inputs, the tool calls, the reasoning, the outcome. Run it inside a contained environment with least-privilege credentials, so a mistake cannot reach systems the agent was never meant to touch.
In the operators we run, evaluation never stops at launch. We keep the test set running against the live agent and watch for drift, because an agent that was correct last month can degrade as the world it acts on changes.
When to build it yourself
You can prototype an agent in an afternoon; making one safe to run in production is a different effort. Building in-house makes sense when the workflow is core to your business and you have the engineering and operations capacity to maintain it. Many firms instead use frameworks for the plumbing, or have the agent built and run for them, so they own the outcome without owning the on-call. Either way the parts are the same — what differs is who maintains the loop once it is live.
What do I need to build an AI agent?
A reasoning model, a set of scoped tools the agent can call, memory for context, a control loop that observes and decides, and a written authority bar defining what the agent may do alone. Plus a test set so you can evaluate it before launch.
Do I need to train my own model to build an agent?
Usually not. Most agents use an existing version-pinned model as the reasoning core and get their behaviour from the goal, the tools, and the prompt — not from custom training. Fine-tuning is an optimisation, not a starting requirement.
What is the hardest part of building an AI agent?
Not the model. It is scoping the goal narrowly, securing the tools so the agent never holds raw credentials, and proving the agent behaves through evaluation before it touches anything that matters in production.
Should I build an AI agent in-house or have it built?
Build in-house when the workflow is core and you can maintain it. Otherwise frameworks handle the plumbing, or the agent can be built and run for you so you own the outcome without the on-call burden.
How do I keep an AI agent safe in production?
Run it with least-privilege, scoped credentials inside a contained environment, log every action, keep a continuous evaluation set running against it, and hold a human boundary around high-cost decisions.
We don't advise on AI. We run it for you.
Proven on your data before you commit.