AI Agent Development Company

Last updated: 11 November 2026

This page is specific to agents. If you are looking for the broader picture of AI work, such as chatbots, document reading or forecasting, start with our AI development page. If your task is a fixed sequence of steps with no judgement needed, a plain workflow is often better, which is covered under business process automation.

Agent, chatbot or workflow: which do you need?

Many good solutions mix the three: a fixed workflow for the predictable 80% of cases, and an agent only for the part that needs judgement. We will recommend the simplest option that works.

When an agent is worth building

• The task needs reading messy input (emails, documents, free-text requests) and then acting in more than one system.
• The steps differ from case to case, so a fixed workflow would need hundreds of branches.
• A person currently spends hours on triage, lookup or follow-up, and the outcome can be checked quickly.
• A wrong action can be reversed or approved by a person before it takes effect.

An agent is the wrong choice when the task is already a clean sequence, when a mistake would be costly and cannot be reviewed, or when you cannot describe what a correct result looks like.

What we build

• Task agents: an agent that takes a request, gathers what it needs from your systems and completes or prepares the work.
• Agents with tools: connections to your software through official APIs, such as looking up a record, drafting a message or creating a task, each with its own permission.
• Assistants inside your product: for example the question-and-answer assistant described in our AI assistant case study.
• Knowledge-grounded agents: answers based on your documents and data, with the source shown so people can check.
• Human-in-the-loop flows: the agent prepares, a person approves, then the action runs.
• Agent workflows: several small steps with clear hand-offs rather than one large instruction, which is easier to test and debug.

How an AI agent project runs

1. Define the job. We write down the task, who asks for it, what a correct result is and what the agent must never do.
2. Collect real examples. You provide real, anonymised cases, including difficult ones. These become the test set.
3. Design the tools and workflow. We list the systems the agent can reach, the actions it can take and where a person must approve.
4. Build a first version. A narrow version that handles the main cases, running on a test environment with read-only access wherever possible.
5. Evaluate. We run the agent against the test set, measure it against the pass criteria and review failures with you.
6. Add guardrails. Limits, approvals, logging and fallbacks are tightened using what the evaluation showed.
7. Pilot, then launch. The agent runs alongside your team on live cases before it is given more responsibility.
8. Monitor and improve. Logs and review of mistakes feed the next round of changes.

Evaluation: how you know the agent works

Most agent demos look good on the three examples chosen for the demo. We treat evaluation as part of the build, not an afterthought:

• A fixed test set built from your real cases, kept separate so it is not tuned to.
• Pass criteria agreed before testing: for example, correct action, correct data, and no action taken when it should have asked.
• Failure review: every failed case is categorised (wrong tool, wrong data, misread request) so fixes are targeted.
• Re-testing after every change so an improvement in one place does not break another.

We report results as what was tested and what passed. We do not promise an accuracy figure before the test set exists.

Guardrails we put around an agent

• Least privilege: the agent only gets the tools and data its job needs, often read-only to begin with.
• Approval steps: actions that send messages, change money or delete records wait for a person.
• Scope limits: clear instructions on what is out of bounds and a fallback of handing over to a person.
• Access rules respected: the agent sees what the signed-in user is allowed to see, no more.
• Logs: what was asked, what the agent did and why, so any decision can be reviewed.
• Cost and rate limits so usage cannot run away unnoticed.

Build vs buy: how to scope an agent project

Ready-made agent platforms and no-code builders are a good fit for simple, low-risk tasks inside one tool. Custom development makes sense when the agent must work across several of your systems, follow your approval rules, or run inside your own product. Ask these questions:

1. Can the task be done inside one existing tool? If yes, try that tool first.
2. Does the agent need to act in systems that have no ready-made connector?
3. Do you need audit logs, role-based access or data to stay in your own accounts?
4. Is the agent part of your product, so customers will rely on it?
5. Can you name the single measure that shows it is working, such as time per case?

Cost depends on the number of tools, how much evaluation the task needs and how much human approval is required, so it is quoted after a scoping call. Model usage, hosting and platform fees are paid by you to those providers and are listed as separate lines.

What you receive

• The working agent, deployed in your accounts.
• The tool definitions, instructions and workflow, documented so your team can read them.
• The test set, the evaluation results and the list of known limits.
• Guardrail and approval settings, with logs you can review.
• Training for the people who will supervise it, and a plan for monitoring after launch.
• The full source code on custom builds, handed over once you clear the agreed milestones.

Risks and how we handle them

Related work

We describe one build in the AI assistant inside business software case study and another in the container trucking dispatch case study. For larger systems around an agent see custom software development, and for the full list see our services.

Questions buyers ask