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Use a deterministic workflow when the steps and branches are predictable. If a mostly fixed process has one step that needs interpretation, keep the workflow and add an LLM-powered step there. Consider an AI agent when the system must decide what to do next—and may need to change its plan or tool choice as new information arrives.
Here, “agent” means a system in which a model dynamically directs its process and tool use within instructions and guardrails. The term is not used consistently across the industry, so the important question is not whether a system is called an agent; it is whether its execution control genuinely needs to adapt at run time.
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What distinguishes a workflow from an agent?
A workflow follows predefined code paths: its steps, conditions, and handoffs are specified in advance. An agent gives a model some control over the sequence of actions, such as choosing a tool, deciding whether the result is sufficient, or determining what information to request next. Anthropic uses this distinction in its December 19, 2024 guide to building effective agents; it also cautions that parts of the tooling landscape have changed since publication, so its architectural distinction is more useful here than any dated tool recommendation.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAn LLM in a workflow does not automatically make the whole system an agent. If the model classifies a request or extracts information, then returns control to a predetermined process, execution is still largely workflow-directed. OpenAI describes this as a rule-based process delegating a bounded interpretation step to an LLM before continuing along the workflow (A business leader’s guide to working with agents, accessed October 7, 2026).
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When do you actually need an AI agent?
Use this test in order. A “yes” to contextual adaptation is not enough by itself: the added flexibility should solve a real task problem and be safe to operate.
- Can you write down the steps and branches reliably before a run? If the process is stable and predictable, start with a deterministic workflow. Fixed paths can make behavior easier to inspect and audit, although rules still require upkeep as conditions change.
- Is only one bounded step interpretive? Keep the workflow in charge and use an LLM for that step—for example, classifying an incoming request, summarizing a document, or extracting fields. Specify what the step should return and what happens when its output is missing or uncertain.
- Must the system choose or revise its next action based on context? An agent becomes a candidate when it must select among tools, respond to exceptions, gather additional information, or ask a clarifying question before it can proceed. OpenAI’s practical guide to building agents, accessed October 7, 2026, identifies nuanced decisions, difficult-to-maintain rule sets, and unstructured data as conditions that may suit agentic systems. These are reasons to assess an agent, not proof that one is necessary.
- Is the benefit worth the operating trade-off? Compare the value of adaptation with added uncertainty, model use, latency, cost, evaluation work, and maintenance. There is no universal cost or speed threshold in the cited guidance; measure the actual task and implementation.
- Can you evaluate the full run and stop safely? Before giving a system dynamic control, define permitted tools, escalation points, run limits, and the conditions for human review. Test representative cases, including failures and ambiguous inputs.
Which design fits your task?
| Design | Who directs execution? | Good fit | Main trade-off |
|---|---|---|---|
| Deterministic workflow | Prewritten rules and branches | Predictable, repetitive work with known steps | Behavior is easier to specify, but rules need maintenance when conditions change. |
| Workflow with an LLM step | The workflow, except for a bounded judgment step | A stable process that needs interpretation at one or a few defined points | Preserves control around the model step, while adding model behavior to test and maintain. |
| Agent | The model dynamically chooses actions and tools within instructions and guardrails | Context-dependent tasks where exceptions, new information, or tool choice can change the next step | Offers more flexibility but requires stronger evaluation, controls, and runtime oversight. |
These are qualitative architectural trade-offs, not universal performance rankings. Actual cost and latency depend on the task and implementation. Google Cloud’s agentic AI design-pattern guidance, accessed October 7, 2026, also highlights evaluation, security, reliability, and cost considerations as architectures become more complex.
How to start without overbuilding
Map the current process
Write down inputs, steps, decision points, outputs, and failure paths. Mark which decisions can be expressed as stable rules and which require interpreting variable or unstructured information. A process that sounds complicated may still have a predictable path; complexity alone does not require an agent.
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Implement known steps and branches as a workflow. If interpretation is localized, put an LLM at that boundary and define its expected output, validation, and fallback. This gives the model room to handle language or ambiguity without letting it redirect the entire process.
Expand control only where evidence points to a gap
If representative cases show that fixed branches cannot handle meaningful exceptions or that the next action depends on information gathered during the run, test a bounded agent design. Begin with one agent rather than adding multiple collaborating agents by default. Anthropic recommends choosing the simplest workable solution and increasing complexity only when needed; Google Cloud likewise identifies extra evaluation, security, reliability, and cost work in more complex multi-agent designs.
What to evaluate before relying on an agent
Judge the end-to-end task, not just whether an individual model response sounds plausible. A run can fail because the system chose the wrong tool, handed off at the wrong time, ignored a guardrail, or continued when it should have stopped.
- Task outcome: Did the run achieve the intended result, and was the result complete enough for its use?
- Action and tool choice: Were the selected actions appropriate, authorized, and relevant to the input?
- Control flow: Were handoffs, retries, clarifying questions, and stopping decisions appropriate?
- Safety and escalation: Did the run stay within its permissions and seek review when required?
- Operational cost: Did the measured flexibility justify the run’s model use, latency, and maintenance burden?
OpenAI’s agent workflow evaluation documentation, accessed October 7, 2026, describes inspecting traces—including model calls, tool calls, guardrails, and handoffs—and using trace grading to locate workflow-level issues. Datasets and repeatable evaluation runs can help compare changes over time. These techniques help diagnose behavior; they do not establish that an agent is inherently reliable. Set expected outcomes and pass criteria for representative cases before comparing designs.
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An agent is not a more advanced version of every workflow. It is a different allocation of execution control. When a task’s path is known, encode it. When a stable path contains a bounded judgment, add a model at that step. When context must determine what happens next, an agent may be justified—provided its flexibility is worth the added uncertainty and you can evaluate and constrain its actions.
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