Autonomy is not automatically an advantage
AI agents are compelling because they can interpret goals, choose tools, and adapt a workflow. Those same qualities introduce uncertainty. If a process does not need that flexibility, an agent can make the system harder to test, secure, and operate.
Avoid an agent when rules are sufficient
If the workflow is stable and can be expressed clearly, deterministic automation will usually be simpler and more dependable. Calculations, eligibility rules, state transitions, and fixed integrations do not become better simply because a model decides the next step.
Avoid an agent when failure is unacceptable
Consequential actions need strong controls. If a wrong tool call could create financial, legal, safety, or operational harm, the architecture may require human approval or a fully deterministic execution boundary.
An agent can still assist with analysis or drafting, but it should not receive broader authority than the system can safely govern.
Avoid an agent when the data is not ready
An agent cannot compensate for missing permissions, inconsistent identifiers, stale knowledge, or undocumented processes. It may hide those problems temporarily behind fluent output.
Avoid an agent when success cannot be evaluated
If the team cannot define an acceptable outcome, it will be difficult to test prompts, tools, plans, and failure behaviour. Begin by clarifying the task and its acceptance criteria.
Consider simpler alternatives
A better search interface may solve a knowledge problem. A copilot may support a human without acting autonomously. A conventional workflow engine may handle repeatable operations. A form and validation rules may be better than free-form language.
Use an agent when the workflow genuinely benefits from contextual interpretation and adaptive planning—and when that flexibility can be constrained, observed, and evaluated.