An AI agent can choose its next action as it works; a fixed pipeline follows steps and branches defined in code. Use a pipeline when the process is predictable and execution order matters. Use an agent when the task is open-ended and its required steps cannot be specified reliably in advance. Many systems combine both: workflow code sets the process and approval gates, while an agent handles selected reasoning-heavy steps.
What distinguishes an agent from a fixed pipeline?
The decisive difference is who chooses what happens next. In a fixed pipeline, the developer defines the steps and execution paths. In an agent, a model can assess the task or the result of an action, then select a further action within the instructions and permissions it has been given.
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An agent commonly works in a loop: interpret a request, choose an action or tool, observe the result, and decide whether to act again or finish. A pipeline may also call models and tools, but those calls do not make it an agent if code still determines the next step.
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When is a fixed pipeline the better choice?
Choose a fixed pipeline when the work is well-defined, its stages can be specified in advance, or consistent order and control are important. It is also the better fit when an ordinary function can reliably handle the task.
- Predictable stages: The same kinds of inputs generally need the same sequence of actions.
- Order matters: A later step must wait for a particular earlier step or check.
- Explicit control is required: You need to define permitted paths, approvals, and recovery points in code.
For example, document review may require a set order: extract information, run specified checks, and then route the document according to defined conditions. A model may assist with a stage, but the workflow can still govern what must happen before and after it. Microsoft uses document review to illustrate work that benefits from a fixed sequence. Microsoft Workflows
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When does an AI agent make sense?
An agent is useful when the task is open-ended and the number or sequence of steps cannot be reliably hardcoded beforehand. It can select an action based on the request and what it learns from earlier results. Anthropic describes this fit as problems where it is difficult or impossible to predict the required steps and hardcode a fixed path. Anthropic: Building Effective AI Agents
That flexibility comes with trade-offs. An agent may take more actions than expected, increasing cost, and errors can compound as it acts on earlier results. Keep its permissions and tools bounded, test it in a sandboxed environment, and add guardrails appropriate to the consequences of failure. Anthropic discusses these risks and recommends testing with guardrails in place. Anthropic: Building Effective AI Agents
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How do workflows, routing, and agents fit together?
Not every task needs a choice between one rigid sequence and a fully agent-directed loop. Several patterns sit between those endpoints:
- Prompt chaining: Break work into known subtasks and run them in a defined order.
- Routing: Classify an input, then send it down the appropriate predefined path.
- Orchestrator-workers: Let an orchestrator determine subtasks that depend on the request, then delegate them to workers.
These patterns answer different questions. If the subtasks are known, chaining can keep execution explicit. If inputs fall into recognizable categories, routing can choose among defined paths. If the work needed depends on the particular request, an orchestrator-worker pattern allows more flexible delegation. Anthropic: Building Effective AI Agents
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How should you decide which parts need autonomy?
Assess each part of the task separately rather than choosing one architecture for the whole system. Ask who should choose the next step, how predictable the work is, whether order must be enforced, what tools and approvals are allowed, how partial failures should be handled, and how much cost or variability is acceptable.
- Specify the required outcome and constraints. Identify mandatory steps, checks, and approval points.
- Use explicit workflow logic for requirements that must always hold. Code can enforce ordering, route known cases, and pause for human approval.
- Use an agent only where the next useful action genuinely depends on reasoning about the request or previous results. Limit the tools and actions available to it.
- Plan for partial failure. Define checkpoints, recovery behavior, and when the system must stop or ask a person to decide.
- Test the complete path, including failures. Compare the flexibility gained with added cost, variability, and error risk.
Microsoft’s workflow guidance frames this as a spectrum: developer-defined paths provide control, while model-directed delegation allows more flexibility. A hybrid can preserve explicit process control while giving an agent discretion inside selected steps. Microsoft Workflows
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What should you check before implementing an agentic workflow?
Framework and service capabilities vary by language and platform, so verify the current documentation for the specific implementation you plan to deploy. Microsoft’s Agent Framework overview, last updated August 25, 2026, says the Go framework is in public preview and some capabilities are not yet available there. Microsoft Agent Framework Overview
Microsoft’s Azure Logic Apps documentation marks its Consumption agentic workflow capability as preview and subject to Azure preview terms. Check the service’s current status and terms before relying on it in a deployment. Azure Logic Apps: What are AI Agentic Workflows?
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