AI agent development
for agents that take real actions

A chatbot answers questions. An AI agent takes actions. Agent development means building a system that can plan a multi-step task, call your APIs and tools to carry it out, check its own work, and hand control back to a person at the points that matter. A support chatbot tells a customer their refund policy; an agent looks up the order, checks eligibility against your rules, issues the refund through your payment provider, and logs it in your CRM. We build the second kind: agents wired into real systems with real permissions, tested against evals, and shipped with audit logging and human-in-the-loop checkpoints.

Three layers. First, least-privilege tool access: the agent can only call the specific functions it needs, with scoped credentials, and destructive actions require a human approval step. Second, guardrails on input and output: prompt-injection filtering, schema validation on every tool call, and hard limits on spend, row counts, and iteration depth. Third, observability: every plan, tool call, and decision is logged and replayable, so when something looks off you can see exactly what the agent did and why. We define these boundaries with you during scoping, before any agent touches production.

We build on the Anthropic and OpenAI APIs, with Gemini where it fits, and use tool-calling and structured outputs as the foundation rather than a heavy framework where one is not needed. For orchestration we use LangGraph or the model providers' native agent tooling, plus vector databases such as pgvector, Pinecone, or Weaviate for retrieval. Model choice is a cost and latency decision made per task: a frontier model for planning and judgement, a smaller fast model for routine steps, and no model at all where deterministic code is more reliable.

A scoped single-purpose agent handling one workflow with two or three tools typically takes 6 to 10 weeks, including the evaluation harness and guardrails. A multi-agent system where several agents coordinate, or one agent spans many tools and systems, runs 12 to 20 weeks. We always start with a one to two week spike on your real data and real tools, so you know the approach works before the full build is scoped and priced.

Yes, and that is the point. An agent is only as useful as the tools it can call. We integrate agents with your CRM, ERP, help desk, databases, internal APIs, and third-party services such as Stripe, Xero, Salesforce, and Slack. Where an API is missing or poor, we build the integration layer first. We have connected production systems across logistics, healthcare research, e-commerce, and property, and the same integration discipline applies to agent tooling.

You do, completely. Source code, prompt libraries, evaluation datasets, orchestration logic, and infrastructure configuration transfer to you as part of every engagement. There is no framework lock-in and nothing that ties the agent back to us after handoff. You can run it, change it, and hand it to another team.

With an evaluation harness built at the start, not bolted on later. We define a golden dataset of representative tasks with known-good outcomes, score the agent on task completion, correctness, tool-call accuracy, cost per task, and latency, and run that suite on every change so a prompt tweak that helps one case and breaks three is caught before it ships. In production we track the same metrics live, plus a human review queue for low-confidence runs. You get a dashboard, not a promise.

Yes. Most of our clients are in the United States, United Kingdom, and Australia. We run agreed real-time overlap hours with each region and communicate over video call and Slack. For UK and EU work we build to GDPR requirements, including where model calls send data and how it is filtered or anonymised first. We have shipped production AI on our own products, Tully AI and Mebag, and for client platforms.

Every build is fixed-price and scoped to requirements. A pilot agent proving one workflow starts after a short scoping call and a paid spike. A production single-agent system covering one department's workflow with full guardrails and evals is the most common engagement. Multi-agent systems that coordinate across several workflows are scoped and priced phase by phase. We do not quote without a requirements conversation, and we will tell you honestly if an agent is the wrong tool for the problem.

Often it is overkill, and we will say so. If the task is a single deterministic step, a script or a workflow automation is cheaper and more reliable. Agents earn their complexity when a task needs judgement across multiple steps, the inputs are unstructured, the path varies case to case, and a human currently spends real time on it. During scoping we map the workflow and tell you whether an agent, a simpler LLM feature, or plain automation is the right call.