An AI agent combines a language model with approved information and tools to carry out a defined task. For a UK business, a useful starting point might be triaging service requests, retrieving internal guidance or preparing a record for review. The workflow needs engineering around the model: identity, integrations, evaluations and visible ownership.
Start with one complete task
Choose a workflow with a clear trigger, useful outcome and known owner. Record the current process and representative examples, including exceptions. Compare a model-based approach with conventional rules and integration before deciding where AI adds value.
Give knowledge and tools clear boundaries
Retrieve only the sources the current user may access. Give each tool a narrow purpose and validate its inputs on the server. Information from documents and websites is untrusted input; it must not be allowed to override permissions or instructions. A model response does not authorise a business action.
Keep consequential actions reviewable
Require explicit approval before sensitive changes, external messages or financial actions. Make proposed actions understandable to the reviewer, including the source information and any uncertainty. Record tool calls and decisions without unnecessarily logging sensitive content.
Evaluate before expanding
Test representative tasks, incorrect information, prompt injection attempts and provider failures. Measure completion quality, correction effort, latency and operating costs. Repeat the evaluation when models, prompts or source material change.
Plan the operation
Set timeouts, execution limits and safe retry behaviour. Agree who investigates failures, maintains connected sources and reviews quality. Begin with a focused pilot, then expand on the evidence from real use.
Questions to take into discovery
- Which task is valuable enough to automate?
- What may the agent read and change?
- Which actions need a person's approval?
- How will quality, cost and failures be monitored?
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