AI Agent Development Services
We build AI agents that do real work — not chatbots that demo well. That means rigorous tool design, orchestration, and evaluation, because an agent in production is only as good as the guardrails around it.
Agents that survive production
An agent that works in a notebook and an agent that works against live systems are different engineering problems. The hard parts are tool design, state and context management, failure handling, and evaluation — the work that does not show up in a demo but determines whether the system can be trusted.
We design custom agents and multi-agent systems around your actual workflows: the tools they can call, the data they can see, the decisions they are allowed to make, and the checks that catch them when they are wrong.
What we build
- —Custom single-purpose agents scoped to a specific workflow and toolset.
- —Multi-agent systems with clear roles, orchestration, and hand-off logic.
- —Tool and function-calling integrations against your internal systems and APIs.
- —Context and memory design so agents stay grounded across long-running tasks.
- —Evaluation harnesses and guardrails to measure and constrain agent behaviour.
Why evaluation comes first
We treat evaluation as part of the build, not an afterthought. Before an agent touches a live workflow, we define what correct behaviour looks like and measure against it continuously. That is what lets you ship an agent you can actually rely on, and improve it without guessing.
What you get
Deliverables
FAQ
AI Agent Development: common questions
What is the difference between a chatbot and an AI agent?
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A chatbot responds to messages. An AI agent takes actions — calling tools, querying systems, and completing multi-step tasks toward a goal. Agents need orchestration, tool design, and evaluation that simple chatbots do not.
What is a multi-agent system?
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A multi-agent system splits a complex task across several specialised agents that coordinate — for example, one that plans, others that execute sub-tasks, and one that verifies results. It helps when a single agent would carry too much responsibility or context.
Which frameworks and models do you use?
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We are model- and framework-agnostic. We choose tools based on the problem — including frontier and open models, and orchestration approaches that fit your latency, cost, and data-control requirements — rather than committing to one stack by default.
How do you keep agents reliable in production?
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With evaluation defined up front, guardrails that constrain what an agent can do, and monitoring that catches regressions. Reliability is an engineering discipline, not a property of the model.
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