Choose based on the task, not the technology: use a script or deterministic workflow when steps and branches are known and inputs are consistent; keep the work manual or require human review when it is sensitive or hard to verify; consider an AI agent when it must interpret ambiguous information and adapt what it does next. Many effective processes combine these approaches.
Start with the task: six questions to ask
- Can you define the steps and branches in advance? If so, a checklist or deterministic workflow is usually the simpler starting point. A system that follows a known path does not need to make runtime decisions about what to do next. Salesforce’s architecture guidance describes traditional automation as a fit for rule-based, predictable work; Google Cloud’s design-pattern guidance lays out the trade-offs between more deterministic and more dynamic patterns.
- How much do the inputs vary? Stable fields and familiar formats are easier to handle with rules. Mixed, unstructured, or exception-heavy inputs may need a bounded model step or an agent that can respond to context. OpenAI’s agent guide identifies nuanced judgment and complex workflows as situations where agents may be useful.
- What is the impact of a mistake? The greater the potential harm, the more important it is to keep a person responsible for review and approval. Microsoft’s decision framework for Copilot or an agent calls attention to task repeatability, impact, error detectability, and time sensitivity.
- Can someone catch an error before it matters? If mistakes are subtle or difficult to detect, build in validation or keep a human-led process. A result that looks plausible is not necessarily correct.
- Does speed matter, and is there time to review? Automation can shorten routine work, but a fast result is not a good trade if the task needs judgment and no one can check it.
- Must the system choose the next step based on what it discovers? If the path is fixed, an agent may add complexity without solving a real problem. If new context or tool results should change the next action, bounded agent reasoning may be justified.
Consider these questions together, rather than treating any single one as decisive. In particular, pair the potential value of faster handling with the cost of errors and the practical ability to review the result.
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What the options mean
Manual workflow
A person carries out the steps and applies judgment. This is a sound choice for unique, exploratory, consequential, or hard-to-verify work. AI can still assist with a draft or summary without taking ownership of the decision.
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Rules handle repeatable steps along a path specified in advance. Use this when inputs, conditions, and expected outcomes are sufficiently predictable. Deterministic automation is often easier to inspect and audit than a process that can choose different actions at runtime.
LLM-powered step
A language model handles a bounded task that calls for interpretation—such as classifying or summarizing text—inside a process whose overall sequence remains specified. This is different from an agent: the model performs a defined part of the workflow rather than controlling its execution. OpenAI explains this distinction in its practical guide to building agents and its guide for business leaders.
AI agent
An agent uses a language model to manage execution, make decisions, select tools according to the current state, and adapt its plan. Its flexibility is useful when a task is ambiguous, multi-step, or difficult to specify completely. Give it explicit tools and guardrails; do not grant open-ended authority just because the task involves AI.
Hybrid workflow
A hybrid assigns each part of the job to the approach that fits it: deterministic steps for stable operations, a model for a bounded interpretation task, an agent for decisions that genuinely need to adapt, and human approval at consequential checkpoints. Salesforce’s architecture guidance discusses combining AI planning with deterministic transaction controls, while OpenAI notes that automations, agents, and model-powered steps can be used together in its business guide.
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| Approach | Best fit | Main caution |
|---|---|---|
| Manual workflow | Unique or exploratory work; consequential decisions; results that are difficult to verify automatically. | Routine work may take longer, but retaining human judgment can be worth that cost. |
| Script or deterministic workflow | Repeatable tasks with stable inputs, known rules, and branches that can be specified in advance. | Unanticipated formats or exceptions may fall outside the rules. |
| LLM-powered step | A bounded part of a known workflow that needs interpretation or judgment. | The model’s output still needs appropriate checks; it does not automatically make the whole workflow adaptive. |
| AI agent | Context-sensitive, multi-step work where the next action depends on runtime findings. | Greater flexibility can bring added complexity, latency, and cost. |
| Hybrid workflow | Processes with both stable operations and specific reasoning or approval needs. | Define handoffs, permissions, and review points so responsibility remains clear. |
These are design choices, not guarantees of performance. Google Cloud describes agent patterns as trade-offs in flexibility, complexity, and performance, and notes that dynamic, multi-call patterns can affect latency and cost in its architecture guidance. Official guidance reviewed here is qualitative; it does not establish a universal success rate or prove that agents outperform scripts or people across tasks.
A practical decision path
- Write down the task’s steps, inputs, and exceptions. If you can specify the path before it runs and the inputs are stable, begin with a manual checklist or deterministic automation.
- Isolate any step that needs interpretation. If the workflow is fixed but one step must make sense of unstructured information, try a bounded model-powered step rather than making the entire process an agent.
- Use an agent only where the path must adapt. Identify what information can change the next action and limit the agent’s available tools and authority to what the task requires.
- Set review and approval points. Decide which results a person must check, especially before a consequential action or where errors are difficult to detect.
- Compare the benefit with operating costs. Account for the task’s value, oversight needs, and the extra complexity, latency, or cost of a dynamic approach.
Keep accountability with people
Automation changes how work is performed; it does not make the result someone else’s responsibility. Microsoft puts it plainly: “Delegating work to AI doesn’t transfer accountability.” A person or organization using AI still needs to decide what to trust, what to review, and who can approve consequential actions. Microsoft’s guidance is especially relevant when weighing speed against impact and detectability.
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