Agentic AI engineering
For teams exploring where AI can make a useful difference.
Choose a question before choosing an architecture.
Responsibilities you can inspect.
- 01
Task & tools
Define the input, allowed tools, output contract and stopping condition for each role.
- 02
Independent checks
Keep deterministic validation separate from model-generated advice where the task permits it.
- 03
Human decisions
Identify consequential actions that require approval and record who may approve them.
Where we can help
- A team considering AI for a defined operational task
- A prototype that needs tool access and clear review points
- A multi-agent workflow requiring roles and evaluation
We assess whether an agent-based approach fits the task, then validate assumptions before expanding the solution. System boundaries, data access, and human review are considered from the outset.
What this can look like
A knowledge workflow could use specialist agents to organise material, compare entries, and prepare a reviewable summary. The design would define data boundaries and a human approval step.
What we clarify first
Model access, third-party service costs, data permissions, and operational requirements are agreed before implementation.
Deliverables are tailored to the agreed scope; the example describes a possible engagement rather than a completed customer project.