Grounded in your systems.
Map the workflow, users, and sources of truth. Connect approved tools and retrieval sources, with clear rules for what information the agent can see and what actions it can request.
Production AI agents
Useful autonomy.
Clear accountability.
AI agent development for work that extends beyond a chat window. We connect agents to your systems and design the controls, evaluations, and user experience they need in production.
The starting point
Start with the work. Then decide what to automate.
For teams building an internal assistant, a tool-using support workflow, a knowledge retrieval system, or an agent that needs to move from a convincing demo into a real operating environment.
An agent can retrieve information, choose tools, and act across a workflow. Each capability introduces decisions about access, data, and failure. We work with your team to decide where autonomy is useful, where a deterministic workflow is clearer, and where a person needs to stay in control.
Boundaries before autonomy.
Scope permissions around the task. Use approval gates for consequential actions, validate tool inputs, and design escalation paths for ambiguity, missing information, and failed operations.
Visible in production.
Agree tracing, error handling, usage limits, and operational ownership. Build an evaluation baseline that checks task completion and failure modes before changes reach your users.
How the work takes shape
Clear scope.
Visible progress.
Each engagement starts with your system, users, and risks. We agree the work and the success criteria before building.
Define the workflow.
Agree the users, task boundaries, integrations, data access, and measurable criteria for a useful result. Identify which actions require human approval.
Build and investigate.
Implement a focused workflow and inspect its behaviour with representative tasks. Test retrieval, tool selection, permissions, recovery, and escalation as a connected system.
Prepare for operation.
Review release criteria, document known limitations, and hand over the code, evaluation suite, integration configuration, and agreed operating procedures.
What you leave with.
A working agent with documented tool boundaries, an evaluation baseline, and a clear operational handover. The exact integrations, deployment environment, and support scope are agreed before implementation.
Can you improve an agent we have already built?
Yes. We can start with the current workflow and architecture, inspect failures and permissions, and agree a focused plan for improving reliability and production readiness.
How do you approach agent security?
We map data and actions, apply scoped access, validate inputs at tool boundaries, and decide where approval is required. We also test adversarial inputs and document limitations. Controls are chosen for the use case and do not eliminate every risk.
Can evaluations be a separate engagement?
Yes. Evals as a service is available for agents and AI applications built by your own team or another provider. We agree the test scope, data handling, and evaluation criteria together.
Let’s build with confidence.
Tell us about your idea or the system you want to improve.