AI Agent Development
We design and ship production AI agents — multi-agent orchestration, tool use, Model Context Protocol (MCP) integrations — then gate every release with the evals that keep them reliable in front of real users, data and money.
- 01
Multi-agent orchestration
Planner–executor and role-based agent systems that break real work into steps, call the right tools, and recover when a step fails — not a single brittle prompt.
- 02
Tool use & MCP integrations
Agents wired to your systems over MCP and typed tool APIs, with the guardrails that stop a tool call from doing something it shouldn't.
- 03
Eval gates in CI
A graded golden set runs on every change, so a prompt or model swap can't silently regress behaviour before it reaches production.
- 04
Discovery & workflow mapping
Before any code, we map the workflow the agent will own — the decisions it makes, the tools it calls and the actions it must never take — so the architecture fits real work, not a demo script.
- 05
Observability & tracing
Every run is logged and traced, so when an agent takes a wrong step you can see which tool, prompt or input caused it — not guess from a black box.
- 06
Handover & ownership
You leave with the code, the eval suite and the CI wiring in your own repository, documented so your team can extend the agent without re-hiring us for every change.
AI agent development, answered
Can you build an agent from scratch?
Yes — from scoping the workflow and choosing models to shipping the orchestration, tools and eval suite. You own the code, tests and evals at the end.
We already have an agent. Can you take it further?
Often. We start with an audit of its behaviour and failure modes, add eval coverage, then extend it on a foundation that won't regress.
How do you stop it from breaking in production?
Every release is gated by evals — hallucination, tool-call safety and regression checks — the same way we gate the classic software we build.
How long does it take to ship an agent?
A focused first version is usually a few weeks — long enough to map the workflow, wire tools with guardrails and stand up the eval suite. Larger multi-agent systems scale up from there; we scope it after a short discovery call.
Which models and frameworks do you build on?
OpenAI, Anthropic and Azure AI, in TypeScript and Python, with orchestration in frameworks like LangGraph and tool access over the Model Context Protocol (MCP). If your stack differs, tell us and we'll be straight about fit.
Who owns the code and the evals at the end?
You do. The agent code, tools, golden set and CI gates are delivered in your repository and are yours to keep, run and extend — no lock-in to us.
Ship the next release without holding your breath.
Tell us what you're building — or what's breaking. We reply within one business day with a concrete plan, not a sales deck.