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AI agent development services

AI Agent Development That Carries Real Business Work

We build AI agents that read the request, look up the facts in your systems, take the routine action and hand the rest to a person with the context attached. Scoped tools, human sign-off, and a log of every decision.

Book an AI agent scoping callSee what we have shipped

What an AI agent is, and is not

An AI agent is software that uses a language model to decide what to do next, then does it through tools: looking up an order, drafting a reply, updating a record. A chatbot answers. An agent works.

That difference is why agents are valuable, and why they fail. The ones that survive production are not the cleverest. They are the ones where someone decided what the agent may do alone, what needs a signature, and what happens when it is unsure.

We build agents for enquiry handling, back-office processing, support operations and internal assistants. The pattern is the same each time: your data, scoped tools, a clear boundary and a record.

What we build

Agents for work that is repetitive, rule-bound and expensive to staff.

Customer enquiry agents

Agents across WhatsApp, web chat and email that resolve routine questions from the real record and escalate the rest with the whole conversation attached.

Back-office process agents

Agents that carry a process end to end: intake, checks, data entry, follow-ups and write-back to the system of record.

Internal assistants

Assistants your staff can ask about orders, accounts, tickets and reports, reading live data rather than a stale export.

Human-in-the-loop approvals

Review queues and sign-off steps, so a person approves anything irreversible and can see why the agent chose it.

Evaluation and testing

Test sets built from your real cases, so you know how often the agent is right before and after every change.

Monitoring and guardrails

Logging, alerts and fallbacks, so you see what the agent did, catch drift early and keep it inside its limits.

An agent needs a boundary, not a bigger prompt

How much an agent may do alone is an engineering decision. We make it explicitly, per action, before the build starts.

  1. 01

    Autonomy decided per action

    Every action is marked automatic, needs approval, or never allowed. The rest of the design follows from that table.

  2. 02

    Scoped tools, not a login

    The agent gets named tools with narrow permissions. It cannot do what no tool allows, whatever it is told.

  3. 03

    Escalation with context

    When confidence is low or a request is unusual, the agent hands over with the conversation and the facts it found, not a cold transfer.

  4. 04

    Every decision logged

    What it read, what it chose and why, in an audit trail you can query. An agent you cannot audit is on trial, not in production.

  5. 05

    Tested before it is trusted

    Evaluations run against real past cases and gate every release, so a model or prompt change cannot quietly make it worse.

Agents we have put into production

Both run inside real operations.

AI agentsConsumer ServicesEight agents juggling two devices each across several WhatsApp numbers, consolidated into one inbox with an AI layer in front that resolves routine enquiries and passes the rest to a person with the full conversation.Read case studySupport operations consoleMeridian ConsoleAn AI assistant over a read-only MCP server spanning four support and finance platforms, with writes refused at the transport.Read case study

How an agent build runs

  1. 01

    Pick one process

    About a week. We choose the process, measure it, and write the boundary table: what is automatic, what needs approval, what is off limits.

  2. 02

    Build the thinnest slice

    The agent, its tools and its logging, running against a real slice of the work instead of a demo.

  3. 03

    Test on real cases

    We replay your past cases through the agent and fix what fails before anyone relies on it.

  4. 04

    Go live, then widen

    A first agent is typically in production in three to four weeks. We widen what it handles as the numbers earn it.

AI agent development, answered

It is building software agents that use a language model to take real actions in your systems. That covers choosing the process, designing the tools and permissions the agent gets, connecting it to your data, testing it on real cases and monitoring it once live.

A chatbot answers questions. An agent also acts: it looks up the record, updates a system, drafts and sends a reply, or escalates. Because it acts, it needs boundaries, approvals and an audit trail that a chatbot does not.

Routine, reversible work with clear rules: answering common questions, looking up status, classifying and routing requests. Anything irreversible, such as payments, deletions and outbound commitments, waits for a person. We write that line down per action before building.

No. It removes the work nobody wanted: retyping data, chasing status, answering the same question for the ninth time. Teams we work with move people onto customers and judgement calls.

No agent is never wrong, so we design for the moment it is. Evaluations on your real cases measure how often it errs, boundaries limit the damage, escalation passes unsure cases to a person, and the log lets you see and fix what happened.

The audit takes about a week and a first agent is typically live in three to four weeks. Cost depends on the number of systems and the actions the agent can take. We scope it after the audit, so you know what you are buying before you commit.

Yes, that is the normal starting point. Where your tools have an API we use it. Where they do not, we build the bridge. Nothing gets migrated.
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Read more
Agentic AIYour agent doesn't need a bigger prompt. It needs a boundary.Most agent projects fail in the same place: nobody decided what the agent is allowed to do on its own. That is an engineering decision, not a prompting one.Testing & GuardrailsAn agent you cannot audit is not in production, it is on trialThe second question every operator asks is "what did it do, and why?" If the system cannot answer that, it does not matter how good the demo was.How We BuildYour first automation should be embarrassingly smallLarge transformation programmes fail quietly for eighteen months. A small slice in production fails loudly in three weeks, which is the useful kind.

Tell us the process you want off your plate.

Thirty minutes with an engineer. You leave with a clear view of what an agent could safely handle in it, and what should stay with a person, whether you hire us or not.

Book an AI agent scoping call