For most of the generative AI era, the dominant interface has been the prompt: a user types a request and the model responds. That model is now giving way to something more powerful — the autonomous agent, which is given an outcome and then selects its own steps across multiple systems to achieve it.
This is not just a UX change. It changes what AI can do, how it is built and how it is governed.
Assistants versus agents
An assistant answers questions or drafts content based on a single interaction. An agent pursues a goal over time. For example, an assistant might summarise a customer email. An agent might read the email, check the order status, initiate a refund if the policy allows, update the CRM and notify the customer — only escalating if the case falls outside its authority.
The difference is scope and persistence. Assistants are interactive. Agents are operational.
Where agents add value
Agents are most useful for workflows that are repetitive, data-heavy and bounded by clear rules. Examples include invoice processing, supplier onboarding, compliance checks, inventory rebalancing and first-line IT support. The common factor is that the workflow has a defined start, a measurable outcome and a set of tools the agent can call.
They are less useful where judgment, negotiation or empathy matter. A agent can schedule a meeting; it should not conduct a redundancy consultation.
Designing a first agent workflow
Start with a narrow, high-volume task that already has documented rules. Map the decisions a human makes, the systems they touch and the exceptions they escalate. This becomes the agent’s instruction set.
Next, define the tool layer. The agent needs secure, scoped access to each system. Use existing APIs where possible, and avoid giving the agent broad credentials. Each tool should do one thing and log what was done.
Then build the control layer. Specify when the agent must pause for human approval, how it reports errors and what happens when it cannot complete a task. A agent without guardrails is a liability.
Finally, measure outcomes. Track completion rate, error rate, escalation rate and end-to-end time. Compare against the human baseline. An agent that is slower or less accurate than a person is not ready for autonomy.
Common failure modes
The biggest mistake is giving an agent too much scope too soon. The second is underinvesting in observability. When an agent goes wrong, you need to know exactly what it did and why. The third is ignoring organisational change. Staff who previously owned the workflow need to understand their new role: exception handler, trainer and auditor, not replaced operator.
The bottom line
Agents represent a genuine step forward in enterprise AI, but they are not magic. They work best when the workflow is well understood, the tooling is secure and the governance is explicit. Build your first agent around one boring, valuable task. Get that right, and the organisation will trust you with the next one.