Practical operating guidance

An AI agent needs a clear job.

Start with a bounded business task, the information it needs and the actions it may take. A useful agent workflow connects assistance with an accountable person and a visible outcome.

This is a planning hub for a scoped software conversation. It is not an agent marketplace, a list of ready-made integrations or a promise of autonomous results. Current capability and delivery scope are confirmed for the proposed workflow.

Discuss an agent workflow

Choose work with a clear finish.

An agent brief is easier to evaluate when the task has a recognisable start and end. Preparing a draft response from approved documents is more specific than asking AI to manage an entire department.

Identify the person who receives the result and what they need to check. If a conventional form, rule or search solves the problem more reliably, include that option in the decision.

Trigger: what starts the workflow and who is allowed to start it?
Context: which information is relevant and permitted?
Action: is the output a draft, a recommendation or an authorised system change?
Owner: who checks the result or takes over an exception?

A practical example: preparing a service-request response.

Consider a team receiving routine service requests. An illustrative agent reads the submitted request, searches an approved knowledge set and prepares a response with source references and a proposed next action.

The team member checks the context before release. An unsupported question, missing account information or a requested action outside the agreed scope goes to the named human route. This example illustrates a design to evaluate, not an available connector or deployed customer system.

Make the action boundary explicit.

A conversation that produces text and a workflow that changes records have different consequences. Specify each permitted tool, the account it uses and the conditions under which it may run.

Keep approval before consequential actions where the workflow requires it. Define how the system recognises a failure, prevents duplicate actions and leaves enough information for the operator to recover. Do not give an assistant broad access simply because the interface makes it possible.

Test the difficult cases.

Use representative, permitted examples with expected outcomes. Include incomplete requests, contradictory documents, unsupported questions and attempts to push the workflow beyond its scope.

Record whether the result is supported, whether an action was correctly held for approval and whether a person can understand what happened. Judge the workflow as a whole rather than relying on a model's general benchmark score.

Compare against the existing process using the same task definitions.
Separate response quality, action accuracy, latency and operating cost.
Review exceptions and unsuccessful runs with the accountable team.
Agree acceptance criteria, monitoring and a route to disable the workflow.

Bring the systems and constraints.

For an initial discussion, describe one task, the current source systems, the people involved and the consequence of a wrong result. State any deadline and access constraints.

A build may involve an application, APIs, a model provider and an operational handover. Integrations, hosting, permissions, data handling and support are decisions for the specific engagement; they are not implied by the word agent.

Start with your
actual requirement.

Tell us the work, people and outcome you have in mind. We’ll confirm current scope and availability before agreeing the next step.