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I no longer treat an AI agent as the default way to automate a task. I start with the task: if its steps are predictable, a direct model call or fixed workflow is often simpler to control. I use an agent when the work is open-ended enough that the model needs to plan, choose tools, or adapt as it goes.
That is an architecture rule, not a claim that agents are inherently bad—or a report of a particular project’s measured results. The practical question is whether autonomy solves a real problem worth its added cost, latency, and risk.
What changes when you choose an agent?
A direct model call asks a model to handle a bounded request and return a result. A fixed workflow follows steps chosen in advance, with code or people controlling the sequence. An agent has more discretion: it can decide how to pursue a goal, select tools, and adjust its process. Anthropic’s description of agent behavior emphasizes that distinction between deciding how to accomplish a task and following a fixed script.
These are not mutually exclusive categories in every implementation. A workflow can include model calls, and an agent can operate inside a carefully bounded workflow. The useful distinction is who—or what—controls the next step when the task proceeds.
When a direct call or fixed workflow is the better starting point
Choose a direct model call for bounded work
If one model response can complete the task, an agent’s planning and tool-selection loop may add machinery without adding useful capability. Summarization, translation, and classifying feedback are examples Google Cloud gives for work that may be handled non-agentically when it is predictable or fits a single model call.
Choose a workflow for repeatable steps
When the sequence is known, encode that sequence. A workflow can make required checks explicit, route exceptions to a person, and avoid asking a model to rediscover the same process on every run. Google Cloud’s architecture guidance says predictable or highly structured work can be more cost-effective with a non-agentic approach.
For a stage with predictable control flow, OpenAI’s Programmatic Tool Calling documentation recommends considering code that performs the work and returns a smaller structured result. Direct tool calling can make more sense when one call is enough, each result needs fresh model judgment, or approval or native artifacts must be preserved. The choice is not simply “agent or no AI”; it can be a model call inside a deterministic program.
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An agent is more compelling when a goal cannot be reduced to a reliable fixed sequence and the system must make decisions along the way. Google Cloud’s examples include checking an order database or calling APIs to synthesize recent research. In such work, the model may need to decide which information to retrieve next or how to proceed when an intermediate result changes the plan.
Before choosing an agent, verify that the task actually requires some combination of:
- Multiple steps whose order depends on what the system finds.
- Tool or external-data use that cannot be fully specified as a fixed sequence.
- Planning or adaptation in pursuit of a goal, rather than just filling a known template.
- A useful outcome that justifies the extra model decisions and operational complexity.
If the steps are stable, or the “autonomy” amounts only to selecting among a few predictable branches, ordinary code and a bounded model call may be easier to reason about.
Compare the architectures against the task
Use the simplest option that meets the task’s requirements. This comparison is a decision aid, not a guarantee: actual performance depends on the implementation and the work being evaluated.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →| Decision factor | Direct model call | Fixed workflow | Agent |
|---|---|---|---|
| Task structure | One bounded request and response | Known steps and decision points | Open-ended goal with steps that may change |
| Who directs the next step? | The caller supplies the request | Code or a person follows the defined sequence | The model may choose tools or adapt its plan |
| Tool use | Often unnecessary, or limited to a sufficient call | Tools are invoked at predefined points | Useful when tool choices depend on intermediate results |
| Control and auditability | Usually a bounded input and output | Steps and checks can be made explicit | Requires visibility into plans, actions, permissions, and exceptions |
| Main trade-off | Cannot independently carry out a multi-step process | May be brittle when the task departs from its defined paths | More decisions can mean more latency, cost, and failure modes |
Google Cloud cautions that a single agent may perform less effectively as its toolset and task complexity grow, including through latency, incorrect tool selection, or incomplete work. Adding tools is therefore not a free way to make an agent more capable.
Count operational costs and error consequences
Do not compare architectures only by whether they can produce a plausible answer. Evaluate them on representative tasks for quality, evidence coverage, token use, latency, cost, retries, recovery, and safety—the dimensions OpenAI recommends measuring in its tool-routing guidance.
Also ask what happens when the system is wrong. A weak summary and an unauthorized financial or account action are not equivalent failures. For sensitive, high-stakes, or hard-to-reverse actions, OpenAI recommends human oversight; Anthropic similarly argues that people should retain control over high-stakes decisions and warns that autonomous systems can exceed what a user intended.
- Estimate whether extra planning and repeated model calls are worthwhile for the task.
- Measure how often the system needs retries, fails to finish, or chooses an unsuitable tool.
- Check whether it preserves the evidence, citations, or artifacts the result requires.
- Set an approval or escalation point before consequential actions.
- Test how it behaves when information is missing, conflicting, or outside the expected path.
Put boundaries around any autonomy you do need
An agent should have only the access needed for its assigned work. Anthropic’s practical guidance discusses action-specific permissions and reviewing plans; its safety framework also highlights transparency and the risk of unintended actions. For an agent that can act, consider using bounded tools, visible progress, and a clear pause point when intent is uncertain or an action has meaningful consequences.
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Approval prompts need judgment, too. Asking a person to approve every harmless step can erase the benefit of autonomy, while allowing an agent to push through every uncertainty risks acting on a misunderstanding. Anthropic captures this tension: an agent that stops at every possible question loses much of its usefulness, while one that always continues can misread what the user intended.
Content an agent reads can also contain malicious instructions intended to redirect its tool use—a prompt-injection risk identified in Anthropic’s practical guidance. Tool access and defenses matter more when an agent can reach more tools or operate in less constrained environments. Do not treat retrieved text as authorization to take an action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Scale up only when evaluations show a need
Start with the smallest architecture that can meet the task’s quality and safety requirements. OpenAI recommends starting with a single agent and moving to multi-agent systems only when needed. Google Cloud notes that multiple agents bring additional evaluation, security, reliability, orchestration, and cost concerns.
If a single agent is not sufficient, identify the measured shortcoming before splitting the work across agents. More components create more handoffs and coordination decisions to test; complexity is justified only when it improves the result enough to outweigh those costs.
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- Write down the task and its completion criteria. If the request has one bounded answer, test a direct model call first.
- Map the steps. If the same sequence works reliably, implement it as a fixed workflow, including explicit handling for exceptions.
- Identify decisions that cannot be fixed in advance. If the system must choose tools or revise its plan based on what it finds, test an agent for that part of the work.
- Set permissions and approval points. Limit tools to what the task needs, and require human review where mistakes could be serious or difficult to reverse.
- Evaluate representative cases. Compare quality, evidence coverage, latency, cost, retries, recovery, and safety. Include ordinary cases and cases with missing or conflicting information.
- Add complexity only to address a demonstrated gap. If the simpler approach misses a requirement, make a targeted change and evaluate again.
The reason I choose workflows before agents is not that autonomy has no value. It is that autonomy is a capability to justify, not a default setting. Use it where the task needs adaptive planning; keep the rest of the process as predictable and bounded as the work allows.
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