Use a fixed automation when the steps and rules are known. Consider an AI agent when the input is variable, the task requires bounded judgement, and the outcome can be checked before it changes anything important.
Classify the workflow first
Write the trigger, inputs, decisions, tools, output, and failure cost. If a decision table covers the process cleanly, deterministic code is usually more predictable, cheaper, and easier to test.
An agent is a fit for controlled ambiguity, such as classifying an enquiry or finding information across approved sources. It is a poor fit for an unbounded instruction with irreversible side effects.
Put boundaries around judgement
Give the model only the tools and data it needs, validate structured outputs, constrain allowed actions, and require approval for high-impact changes. Treat retrieved text as data, not as trusted instructions.
Log the prompt context, tool calls, result, reviewer action, latency, and cost. Without that trail, a successful demo cannot become a dependable process.
Evaluate the outcome
Create a test set from real anonymised cases, including difficult and adversarial examples. Measure accuracy, refusal quality, escalation rate, review time, cost, and downstream business effect.
Release gradually with a fallback path. The system should fail visibly and recoverably, not quietly invent confidence.
An agent is not an upgrade from an automation. It is a different reliability tradeoff. Choose it only where flexible interpretation creates enough value to justify evaluation, controls, and review.
What is the difference between an AI agent and an automation?
A fixed automation follows defined rules and steps. An AI agent can interpret variable inputs and choose among bounded tools or actions, which adds flexibility and uncertainty.
Should an AI agent be allowed to send messages automatically?
Only after the risk, permissions, validation, and review threshold are clear. External or irreversible actions should usually have approval or a narrow allow-list.
How do you measure an AI agent in production?
Track task success, factual or policy errors, escalation, reviewer effort, latency, cost, tool failures, and the business result. Model quality alone is not enough.
Turn the research into a working plan.
Bring the evidence, constraints, and next decision. We will help you turn it into a clear technical brief.