A simple example
An agent might investigate a reporting problem and prepare findings. Giving it authority to change advertising budgets is a separate access and control decision.
Distinguish an agent from a fixed workflow
Anthropic describes workflows as predefined paths and agents as systems where the model directs the process and tool use. This is a useful working distinction, rather than a universal naming standard. A system can combine both approaches.
Choose a bounded first use
A research assistant that prepares a draft for review is easier to supervise than a system that changes advertising spend automatically. Define the task, permitted tools and completion criteria. Keep consequential actions behind explicit controls, and provide a way to stop or undo the operation.
A business acceptance checklist
- Can the task be evaluated against real evidence?
- Are data access and tool permissions appropriately limited?
- Can a reviewer inspect what changed and why?
- Are failure, timeout and escalation conditions defined?
- Does a simpler workflow perform the task adequately?
- Is the cost of errors included in the comparison?
Measure the whole outcome
Evaluate correct completion, review effort, latency and cost. A fast draft that needs extensive correction may not save time. Treat production access as a separate deployment decision after testing. These are implementation criteria, not a claim that XnMark has deployed a particular agent for a named client.
Set boundaries before increasing autonomy
An agent can select actions as it works toward a goal, which makes its operating boundaries important. A research assistant that drafts a summary has a different risk profile from a system that changes budgets or sends customer messages. Define the allowed tools, approved data, actions requiring review and conditions that should stop execution. The same model can support very different workflows depending on those permissions.
Begin with a small, observable task and compare the result with a simpler workflow. Keep an activity record that helps a person understand what happened, including failed actions. Test ambiguous instructions, missing information and tool errors before expanding responsibility. A demonstration that completes one task does not establish dependable performance across all cases. Name the owner who will review exceptions and maintain the process when tools, business rules or source data change.
- Allow only the actions and information required for the defined task.
- Set review points before external publication or material business changes.
- Evaluate quality, failure handling and operating cost against the simpler alternative.
Compare agents and workflows →
Frequently asked questions
What are AI agents?
An AI agent uses a model to choose steps and tools while pursuing a task. The term is used broadly; the important business question is what the system can decide, access and change, and how its work is checked.
When is an agent useful?
An agent can be useful for a bounded task that needs context-dependent choices. Define permissions, review points and failure handling before giving it operational responsibility.
How should an agent be evaluated?
Assess the complete outcome, including accuracy, correction effort, cost and failures. A fluent response is not evidence that the task was completed correctly.
Sources and further reading
Primary material for the concepts discussed here. Our practical checklists and illustrative examples are editorial recommendations. Source links open in a new tab.