An agent uses AI to work through a task and may choose tools, retrieve information, or perform actions. A conversational interface alone does not make a system an agent; the key distinction is how it selects and carries out steps.
Separate goals, reasoning, and execution.
An agent receives a task, uses context to select a next step, and may call a tool before deciding whether to continue. The application controls which tools exist and which identities can use them. The model's choice is therefore only one part of the execution path.
A conventional workflow follows predefined transitions. An agent may help when the next step depends on interpreting varied information, but that flexibility adds uncertainty. A mixture of fixed workflow steps and bounded AI decisions is often easier to operate than an unrestricted loop.
Define the action contract.
A tool should expose a clear operation, expected inputs, and a constrained effect. Separate searching for a record from modifying it. Validate record identifiers, allowed fields, and authorization in the system that performs the action rather than trusting the agent's explanation.
Delegated user access and application-level access behave differently. Confirm which identity is used for each operation, how its permissions are limited, and what happens when the user or business assignment changes.
Example: resolving a service request.
An agent could classify a request, retrieve relevant guidance, and propose a resolution. A deterministic workflow can then route the proposal for approval. Only the approved operation should be sent to the business system, and a failure should return to an owned exception path.
If a tool times out after submitting an update, the application must determine whether the change occurred before retrying. Otherwise, apparently helpful persistence can create duplicate tickets, messages, or transactions. Completion criteria should describe the actual business state.
Bound autonomy and recovery.
Set limits on attempts, elapsed time, and permitted actions. Provide a handoff when evidence is missing or the task falls outside scope. Evaluate normal, denied, ambiguous, and interrupted tasks, and retain enough trace information to explain outcomes. Agent 365 and development frameworks support different parts of this lifecycle; coverage depends on the platform and configured integrations.
Further reading
For task boundaries and decision rights, read AI Governance. For tool access and safe operation, read AI Security.