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What is an agentic workflow?

An agentic workflow is a workflow where an AI system can decide and take multi-step actions toward a goal, instead of only producing a one-shot answer; you may also hear this called an agentic system.

Why it matters

Use an agentic workflow when the task is not just “generate text,” but “figure out what to do next.” That includes tasks like investigating an issue, pulling together information from multiple tools, retrying after failure, or following conditional branches based on intermediate results.

In practice, this is useful when:

• the right next step depends on earlier findings,
• the work spans multiple tools or systems,
• you want the model to plan, act, observe results, and adjust,
• a fixed prompt or single API call is too brittle.

A plain LLM call is good for direct outputs. An agentic workflow is better when the model must operate in a loop.

How it works

At a high level, an agentic workflow adds a control loop around the model:

1. Set a goal or task

   • Example: “Investigate why build failures increased this week.”

2. Let the model choose actions

   • It may decide to search logs, query metrics, read docs, or call an API.

3. Observe the results

   • The system feeds the tool outputs back to the model.

4. Repeat until done

   • The model may refine its plan, take another action, or stop and return a final answer.

This can be implemented in many ways: a simple loop, a planner-executor setup, branching workflows, or more autonomous “agent” patterns. The stable idea is the same: the model is not just generating content; it is driving a sequence of decisions and tool uses.

Tiny concrete example

Task: “Find the latest status of order #1234 and tell me if it shipped.”

A simple agentic workflow might do this:

• Model: identifies that it needs the order system.
• Tool call: get_order_status(1234)
• Tool returns: status = packed, tracking not created
• Model: decides it is not shipped yet and can answer clearly.

If the first tool call fails, the workflow may retry, ask for a different identifier, or check another system.

Common pitfalls / when NOT to use it

• Using an agent when a simple call is enough.
  If you already know the exact input and output format, a direct prompt or function call is often simpler, cheaper, and easier to test.

• Over-automating uncertain tasks.
  Agentic workflows can wander, loop too long, or make poor intermediate choices unless you add guardrails.

• Weak tool boundaries.
  If tools are broad or unsafe, the agent can do the wrong thing quickly.

• No stop condition.
  You need clear limits on steps, time, budget, and escalation.

• Confusing “agentic” with “autonomous.”
  In practice, many agentic workflows are only partly autonomous and still depend on explicit orchestration, policies, and human review.

If the task is deterministic, high-stakes, or easy to script, a non-agentic workflow is often the better default.

Related terms

• What is an AI agent?
• What is a ReAct agent?
• What is the agent loop (perceive-plan-act)?
• What is tool use in AI agents?
• What is a multi-agent system?
• What is agent memory (short-term vs long-term)?
• What is a planner-executor agent?
• What is human-in-the-loop for agents?
• What is an agent harness / runtime?
• What is reflection / self-critique in agents?
• What is task decomposition?

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