AI AGENT DEVELOPMENT
Most AI assistants produce text. An AI agent produces an outcome, it takes a goal, breaks it into steps, uses tools to complete each step, and returns a result rather than a recommendation for what a human should do next.
We design and build custom AI agents for the specific workflows where autonomous execution replaces the manual coordination your team currently provides. The agent handles the retrieval, the decision routing, the API calls, and the output, your team handles the work that requires human judgement, which is a smaller set than most organisations have assumed.
An agent deployed in a workflow changes the economics of that workflow. The question is not whether AI agents will be part of your operations, it is whether you design them around your specific processes or inherit someone else’s defaults.
WORKFLOW ANALYSIS & AGENT SCOPING
We map your target workflows in detail, decision points, tool dependencies, exception handling requirements, and where human judgement is genuinely necessary versus habitual. The scoping output determines whether an agent is the right solution and what its capability boundaries need to be before a line of code is written.
TOOL & API INTEGRATION
An agent’s capability is determined by the tools it can use. We build the integrations between the agent and your existing systems, internal APIs, third-party services, databases, communication platforms, with the authentication, rate limiting and error handling that makes them reliable rather than functional only under ideal conditions.
DEPLOYMENT & MONITORING
We deploy agents with logging, alerting and performance monitoring configured from day one. An agent running in production without monitoring is one where failures accumulate silently. We instrument agents to surface what they are doing, when they are failing, and why, so the deployment is observable rather than a black box that produces outcomes without explanation.
AGENT ARCHITECTURE DESIGN
We design agent architectures, single-agent, multi-agent, with or without human-in-the-loop checkpoints, based on the specific requirements of your workflow. Architecture decisions made without reference to your reliability requirements, error handling needs and tool integration constraints tend to produce agents that work in a demo and fail in production.
AGENT TESTING & EVALUATION
We test agents against the full range of scenarios they will encounter in production, including edge cases, ambiguous inputs, API failures and concurrent execution, and establish performance baselines before deployment. Agents deployed without this kind of evaluation tend to produce failures that are difficult to diagnose because the failure mode was not anticipated in the design.
AGENT MAINTENANCE & EXPANSION
Agents degrade as the systems they connect to change, as prompt behaviour shifts with model updates, and as new edge cases emerge from production use that were not present in testing. We provide maintenance that keeps agents performing against their original specifications and expansion services that extend agent capability to adjacent workflows as confidence in the initial deployment grows.
We build agents that complete workflows rather than agents that suggest what the human should do next. The distinction determines whether the agent replaces coordination overhead or simply adds a new interface to the same manual process.
Autonomous execution is appropriate for the parts of a workflow where the decision can be defined. Where a decision requires judgement that cannot be specified in advance, we design explicit human checkpoints rather than building an agent that makes consequential decisions it is not equipped to make.
An agent running in production without monitoring is a liability. Every agent we build is instrumented with logging, performance tracking and failure alerting, so your team knows what the agent is doing, when it is struggling, and what it needs from you before the problem escalates.
