AI · Practical guide
When an AI agent is the right choice for workflow automation
Choose between rules, an assistant, and an agent by examining the task, available tools, error costs, and the level of human review needed.
An AI agent can interpret a request, gather context, choose tools, and coordinate several steps. That flexibility can be useful when work involves varied language or documents and the next action depends on what the system finds. It also introduces failure modes that a fixed workflow may not have.
The starting point is the activity your team wants to improve. Map that activity before selecting a framework or model. The right solution may be a conventional integration, a knowledge assistant, a predictive model, an agent, or a combination with clearly defined responsibilities.
Match the automation to the work
Separate predictable steps from uncertain ones
List the inputs, decisions, tools, outputs, and exceptions in the current process. Identify which steps follow a stable rule and which require interpretation. A fixed validation or a scheduled data transfer usually does not need an agent.
An assistant may be enough when the goal is to help a person find information or draft a response. An agent becomes more relevant when the system must decide which approved tool to use, gather information from several places, or adapt its next step based on the result. Keep deterministic steps deterministic where that improves reliability.
Choose a narrow first workflow
A useful pilot has a recognisable start and finish. Examples include preparing a request for review, extracting fields from a document, or gathering information needed for a recurring report. Avoid beginning with a broad instruction to automate an entire department.
Define a successful outcome, the inputs the system can expect, and the cases that should be escalated. Agree on what the agent may read, what it may change, and which actions require confirmation. These boundaries shape the architecture and the evaluation.
Connect approved knowledge and tools
For a knowledge assistant, identify the source of truth, how information is refreshed, and which users may see it. Retrieval should respect those permissions. Source references are useful only when they support the answer; an attached link does not by itself establish that the response is correct.
For tools, use narrow interfaces with explicit inputs and validated outputs. A tool that can search an approved knowledge base is different from one that can run unrestricted queries or send messages. Give the system the minimum access needed for the agreed workflow, and keep credentials outside prompts.
Plan human review around consequences
Human review is most useful at a meaningful decision point, with enough context to judge the proposed action. Show what the agent found, the change it proposes, and any uncertainty. Avoid approval prompts that ask a person to confirm an action without understanding it.
Hypothetical example: an operations agent reads a request, retrieves an order record, and drafts an update. A person checks the proposed update before it is sent. The pilot can evaluate retrieval accuracy, draft usefulness, and review time without assuming that every action should immediately become autonomous.
Evaluate whole tasks, including failures
Build a representative set of requests and expected outcomes. Include missing information, conflicting records, unavailable tools, ambiguous instructions, and content that tries to redirect the agent away from the user's task. Test whether the system recognises when it cannot complete the work.
Measure task completion, grounded answer quality, tool choice, latency, and operating cost. Record how often human review changes the output and why. A fluent response or a successful individual tool call does not prove that the end-to-end task was completed correctly.
- Does the result satisfy the original request?
- Are factual claims supported by the available sources?
- Were the right tools used with valid inputs?
- Did the agent stop or escalate at the agreed boundaries?
- Can a reviewer understand the evidence behind the proposed action?
Make retries and partial completion safe
Workflows can fail after some steps have succeeded. Design for timeouts, duplicate requests, and interrupted runs. Where a tool changes records, use idempotency or another explicit duplicate-prevention mechanism. A retry should not silently repeat a consequential action.
Keep a clear record of completed steps and errors. Define when the system should retry, ask for missing information, or hand work back to a person. This operational design is as important as the prompt because it determines how the workflow behaves outside a demonstration.
Run a pilot with a fair comparison
Compare the pilot with the current process using representative work. Include the time spent checking and correcting outputs, not only the time the model spends generating them. Track quality, completion, rework, and actual usage alongside cost.
A pilot may show that a smaller assistant delivers most of the value, that a data integration is the real bottleneck, or that the workflow needs clearer rules first. Those are useful findings. Expansion should follow evidence that the system helps the team under real operating conditions.
What to bring to a discovery conversation
Describe the recurring task, its frequency, the systems involved, and the output a person needs. Bring anonymised examples of normal and difficult cases, the current review process, and the actions that must remain under human control. That is enough to begin assessing feasibility and defining a focused prototype without committing to a particular toolset.
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