Capability / Agentic AI
Intelligence that takes action.
We design and engineer agents that use tools, access information, interact with business systems, and execute multi-step workflows — with the constraints and oversight that production demands.
Foundations
What it takes to make agents useful.
What AI agents are
Software that uses a language model to decide which actions to take — calling tools, retrieving information, and producing outputs — within boundaries you define.
When agentic workflows fit
Multi-step tasks with variable inputs, where steps depend on intermediate results. If the path is fixed, a conventional workflow with targeted AI steps is often better.
Tool use & integration
Agents act through typed tools: APIs, databases, search, internal services. Each tool is scoped, permissioned, and logged.
Knowledge retrieval
Retrieval-augmented generation grounds answers in your approved sources, respects access rules, and returns citations.
Human oversight
Approval gates on consequential actions. Reviewers see the proposed action, the evidence, and the trace.
Evaluation & monitoring
Evaluation sets built from real cases, regression checks on every change, and production tracing to catch drift.
Guardrails & governance
Input and output filters, policy checks, rate and cost limits, and clear ownership of each agent.
Production deployment
Versioned prompts and tools, staged rollouts, fallbacks, and runbooks — the same rigour as any production service.
Architecture
Single agent or many?
More agents isn't automatically better. We choose the simplest architecture that reliably does the job.
Single-agent
One reasoning loop with a focused toolset. Easier to evaluate and debug. Right for most well-scoped tasks.
Multi-agent orchestration
Specialised agents coordinated by a planner. Useful when tasks need distinct skills, permissions, or validation stages.
Illustrative workflow
An agentic workflow, stage by stage.
Tools & data
Execution architecture
A goal. A coordinated system.
Agents reason with models, retrieve from memory, and call scoped tools. Evaluation can send work back for another pass; sensitive actions go to a human.
Request → orchestrate → evaluate → act
Stage 01 / 8
Request
A user or system submits a goal — e.g. “Prepare a renewal summary for account X.” The request is captured with context, permissions, and constraints.
Practical examples
Where agents earn their place.
Illustrative scenarios — the right design depends on your data, systems, and risk tolerance.
Contract intake
Extract key terms, compare against policy, route exceptions to legal for approval.
Support escalation
Gather account context, draft a resolution, and hand off with a summary when confidence is low.
Market research
Collect sources, summarise findings with citations, and assemble a draft brief for an analyst.
A note on autonomy: we design agents to act within explicit limits and to hand off to people when stakes or uncertainty are high. Autonomy is a dial, not a goal.
Have a workflow that should run itself — safely?
Tell us about the process, the systems involved, and where people need to stay in the loop.