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Don’t use an AI agent when a fixed workflow or deterministic program can meet the task reliably. Agents are most useful when a system must interpret context, handle exceptions, or decide what to do next based on what it discovers. Their flexibility comes with added cost, latency, complexity, and risk.
What makes a system an AI agent?
An AI feature is not automatically an agent. In OpenAI’s practical guide to building agents, an agent uses a large language model to manage a workflow and make decisions, using tools to gather context or take actions. A chatbot or a single LLM call does not meet that guide’s definition.
The useful distinction is who determines the next step. In a predefined workflow, code specifies the path. An agent dynamically directs its process and tool use. Anthropic explains this distinction in Building Effective AI Agents.
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The steps and rules are stable
If the task has known steps, structured inputs and outputs, and rules that can be stated clearly, start with ordinary code or a conventional workflow. A fixed path is easier to inspect and predict than one in which a model decides what to do next. OpenAI advises that a deterministic solution may suffice when a use case does not clearly meet its criteria for an agent.
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The task is a fixed sequence that needs language skills
A task can need an LLM without needing an agent. For example, a system might classify a message, draft a response, and send the draft for review in a sequence specified by code. That is an LLM workflow: the model handles language tasks, while the application controls the order, checks, and transitions.
The next step does not depend on discovery
If you can reliably determine the next operation in advance, agent autonomy may add little value. A model-directed loop introduces more ways for a run to vary, without necessarily helping it complete the task better.
When an agent may be worth evaluating
Consider testing an agent when the task requires contextual judgment, involves substantial unstructured information, depends on exceptions that are difficult to maintain as explicit rules, or has an unpredictable sequence of steps. In these cases, a system may need to interpret what it finds before choosing what to do next.
That does not establish that an agent is the right answer. Compare it with a simpler baseline on real routine cases and exceptions. The architecture decision depends on the task’s quality requirements, predictability, latency and cost limits, consequences of failure, tool permissions, and availability of human review.
Choose a starting architecture by task shape
| Task condition | Likely starting point | Reason |
|---|---|---|
| Known, stable steps; clear input and output structure | Deterministic code or workflow | The execution path is explicit and predictable. |
| Fixed sequence with a step that needs language understanding or generation | LLM workflow with explicit stages and checks | The model handles language tasks while code controls the process. |
| Contextual interpretation, exceptions, or substantial unstructured input | Evaluate an agent against a baseline | These conditions may benefit from model-directed decisions, but must be tested on the intended task. |
| Next steps depend on discoveries and cannot be reliably hardcoded | Consider an agent | Dynamic direction may suit open-ended work with unpredictable steps. |
| High-impact tool actions or untrusted source material | Restrict autonomy and add checks and human control | Errors or manipulated instructions can lead to unintended actions. |
Account for tool access and failure impact
An agent with tools can turn a mistaken interpretation into an external action. Untrusted content may also attempt to manipulate the agent’s instructions. OpenAI discusses these risks and mitigations in its safety guidance for building agents; NIST’s Lessons Learned from the Consortium: Tool Use in Agent Systems describes security and reliability risks in systems that act through software tools.
Reduce exposure by limiting tools to the actions the task actually requires, separating untrusted content from instructions, validating outputs, and requiring review before consequential actions. No one safeguard makes an agent safe in every context: design controls around what the system can access, what it can change, and how errors are detected or reversed.
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Use multi-agent designs only for a demonstrated need
Multiple agents add coordination and more interactions to manage. Consider that structure only when a single agent has a demonstrated weakness, such as difficulty with complex logic or tool selection. Otherwise, first establish that an agent is needed at all, then test whether adding another agent improves the task enough to justify the extra complexity.
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What to test before committing
OpenAI’s agent-building guide puts the decision plainly: “Before committing to building an agent, validate that your use case can meet these criteria clearly. Otherwise, a deterministic solution may suffice.” In practice, compare candidate designs using representative examples and exceptions, and assess:
- Task quality and whether the system handles exceptions correctly.
- Predictability and how easily a run can be inspected.
- Latency and cost within the limits of the application.
- What an error can affect, including the impact of tool actions.
- Whether tool permissions can be constrained to the task.
- Whether people can review consequential decisions and recover from mistakes.
The exact architecture cannot be chosen without knowing the task, its error tolerance, data boundaries, available tools, and review requirements. Choose the least complex design that meets those requirements, and increase autonomy only when testing shows a clear need.
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