What Are Agentic Workflows? A Practical Guide to Design and Evaluation

An agentic workflow is a sequence of steps in which an AI agent makes some decisions about how to reach a defined outcome, while the surrounding application sets the task, checks results, and controls when work ends. The design challenge is not to make every step autonomous; it is to decide where judgment adds value and where a fixed rule is more dependable.

Key Takeaways

• Define the outcome first. Specify the deliverable, required evidence, and conditions that make a result acceptable.
• Allocate decisions deliberately. Use agent judgment for ambiguity; use deterministic logic for known rules, state transitions, and measurable checks.
• Make progress observable. Track intermediate results and evaluate both the final outcome and the path taken to produce it.
• Design stopping conditions. End on success, escalate when information is missing, and cap time, cost, and iterations.
• Start with the simplest useful design. Add loops or multiple agents only when tests show that a simpler workflow falls short.

What Are Agentic Workflows?

An agentic workflow combines a goal, available information, and a sequence of actions with at least some model-directed choices about the next step. For example, an agent preparing a research brief might decide which unanswered question to investigate next, then return a draft for validation. The application can still prescribe the required output and limit the number of investigation steps.

The terminology is not universal. Anthropic distinguishes predefined workflows, in which code orchestrates model calls and tools, from agents that dynamically direct their own process. In practice, a single application can combine both: a fixed process may contain an agent-led research step, followed by deterministic validation.

A conventional automated workflow follows predefined transitions: when a form arrives, extract its fields and move it to a queue. An agentic step is useful when the next action depends on interpreting less predictable information—for instance, identifying which missing detail in a free-text request needs clarification. The boundary between the two is a design choice, not a requirement to make the entire process autonomous.

A useful workflow specification contains five parts:

1. Goal and inputs: what the run must accomplish and what information it receives.
2. Steps and decision points: which work is fixed and where the agent may choose a path.
3. State: the evidence, outputs, and decisions the next step needs.
4. Checks: how the application or a reviewer assesses intermediate and final results.
5. Stop conditions: when to complete, return a partial result, or hand the task to a person.

How to Design an Agentic Workflow

Start with a task a team already performs and specify what a successful result looks like. Then turn that task into a small sequence of observable steps. The example below is a research brief, but the same design process applies to triage, analysis, and other information-heavy work.

1. Define the outcome and its evidence

Replace a broad goal such as “research this market” with a concrete deliverable: “Produce a short brief answering three specified questions, with a source for each material claim and a list of unanswered questions.” Define who will use the result and what they need to do with it.

Separate completion criteria from quality criteria. A brief may be complete when all requested sections exist; it is acceptable only if its claims are supported and the evidence is relevant. If a required fact cannot be established, an acceptable result may be an explicitly incomplete brief rather than a fabricated answer.

2. Break the task into steps

A first version might identify the questions, retrieve relevant material, extract evidence, draft the brief, and check it. For each step, record its input, expected output, and failure condition. Keep intermediate outputs small and structured enough for the next step to inspect.

For example, an evidence item can contain a claim, source reference, relevant excerpt, and an uncertainty note. A final drafting step can then use those items rather than relying on an unstructured history of everything the agent has seen.

3. Assign control at each decision point

Decide which transitions are fixed and where the model can choose. The application might always require a source check before a draft is marked complete, while allowing the agent to choose which unanswered question to investigate next. Name the possible next states, including an unknown or needs review state; do not force ambiguous inputs into an arbitrary category.

4. Define shared state and checkpoints

The workflow needs a record of the original task, collected evidence, completed steps, unresolved questions, and current status. Give each step only the context it needs, and make it clear which information is source material and which is an instruction from the application.

At a checkpoint, evaluate a specific condition: “Does each answer have a supporting source?” If the check fails, the workflow can perform a bounded revision or return the unresolved gap. A checkpoint is useful only when its failure has a defined next action.

5. Set termination conditions before running

Define what happens when the workflow succeeds, cannot proceed, or uses its allotted resources. A research run might stop with a complete brief when every required question has evidence; stop with gaps when evidence is unavailable; or stop and hand off when the task cannot be interpreted reliably. A time, cost, or step limit should also terminate the loop with a visible status, not silently label unfinished work as complete.

Which Steps Should Be Agent-Led or Deterministic?

Give the agent work that benefits from interpreting language, weighing incomplete evidence, or selecting among meaningful next steps. Keep a step deterministic when the expected behavior can be expressed as a clear rule and tested directly.

Good candidates for agent judgment

• Interpreting a request whose wording varies and identifying missing context.
• Comparing source passages and explaining how they relate to a question.
• Choosing the next research question from an explicitly bounded list.
• Drafting a response from collected evidence and identifying uncertainty.

These decisions are still fallible. Give the agent an allowed set of outcomes, request an explanation or evidence where useful, and provide an escalation path when the result is ambiguous.

Good candidates for fixed logic

• Checking required fields and the shape of a structured result.
• Calculating a threshold from supplied numeric values.
• Tracking how many iterations remain and enforcing a deadline.
• Applying a defined pass/fail rule and recording the workflow’s status.

For example, an agent may assess whether a source passage addresses a question, while code checks that the resulting evidence record includes a source reference. The presence of a reference does not prove that the source supports the claim; that requires a separate relevance check.

When not to use an agent

If the same sequence works for every input, a script or conventional workflow may be sufficient. If the task is simply to transform a known input once, try a single model call before introducing planning and loops. Anthropic’s workflow guidance recommends starting with the simplest solution that meets the task’s requirements and adding complexity only when needed.

Common Agentic Workflow Patterns

The right pattern depends on how much of the path is known in advance. These patterns can be combined, but each additional branch or loop creates another behavior to test. Anthropic’s overview describes several of these building blocks in detail.

Sequential steps with checks

A fixed sequence passes the output of one step to the next. For a brief, that might mean extract evidence, draft, then validate. Use it when the stages are known and each stage has a clear output. If the evidence check fails, return to collection or end with a gap rather than passing an unsupported claim forward.

Decision-based branching

A classification step selects among known paths. For example, a ticket might go to a billing, technical, or unclear-information path. The classifier can use an agent to interpret the request; the application decides what each branch does and where uncertain classifications go.

Parallel work and aggregation

Independent questions can be investigated separately, then combined into one result. This is useful when one subtask does not depend on another. The aggregation step must still handle conflicting findings, missing results, and duplicate evidence; multiple outputs are not automatically independent confirmation.

Bounded evaluator–revision loop

A draft is checked against a defined rubric, then revised if a specific criterion fails. For example, a reviewer may flag an unsupported sentence and ask the drafting step to remove it or supply evidence. Set a maximum number of revisions and a clear handoff when the rubric still fails; repeated self-critique is not proof of correctness.

Dynamic planning

For less predictable tasks, an agent can choose or revise subtasks as it learns more. Use this only when fixed steps cannot reasonably anticipate the work, and constrain it with a defined objective, observable progress, and explicit limits. Compare it against a simpler baseline before deciding that the extra flexibility is worth the added complexity.

Agentic Workflow Examples

These hypothetical examples show how to divide responsibility. They are design sketches, not reports of measured results.

Example 1: Research brief with evidence checks

Goal: Answer three questions about a market and return a brief with source references.

1. The application checks that the questions and requested format are present.
2. The agent selects a question, finds relevant material, and records candidate evidence.
3. A check confirms that each evidence record contains the required fields; a separate review assesses whether the source actually supports the claim.
4. The agent drafts the brief and identifies unanswered questions.
5. The application stops when the acceptance criteria are met, or returns a partial brief when the allotted steps are exhausted.

Design choice: Research direction is agent-led; required evidence fields and run limits are fixed. If two sources conflict, the brief should describe the conflict rather than invent a resolution.

Example 2: Support-ticket triage

Goal: Prepare a ticket for the right support queue, with a concise summary and any missing details.

1. The application collects the ticket and any required context.
2. The agent proposes a category and summarizes the issue using the supplied text.
3. Fixed logic checks that the category is one of the allowed values and that the summary is present.
4. If the category is unclear, the workflow sends the ticket to a general review queue; otherwise, it moves to the corresponding queue.
5. The run ends when the ticket reaches a queue, or reports that it could not be classified.

Design choice: Interpreting varied customer language is agent-led; allowed categories and the unclear-case path are deterministic. Evaluate the classification against labeled examples, especially tickets that resemble more than one category.

Example 3: Document review and revision

Goal: Produce a draft that follows a supplied checklist without changing its factual meaning.

The agent reviews the draft against each checklist item and proposes edits. A fixed check verifies that all required sections are present; a reviewer assesses whether the edits preserved meaning. If a revision creates a conflict or the checks still fail after the allotted attempts, the workflow stops with the issues listed for a person.

This pattern separates stylistic judgment from measurable structure. It also makes the decision to stop part of the design rather than an improvised response to a stalled loop.

How to Evaluate an Agentic Workflow

Evaluate the complete task, not just whether the final response sounds convincing. An assessment should cover the result, the decisions and intermediate steps that led to it, and the resources used. Anthropic’s guide to agent evaluations distinguishes the run’s trajectory from its actual outcome—a useful distinction when a workflow reports success but the expected result is absent.

Build a small, representative test set

Collect examples of ordinary tasks, ambiguous requests, missing inputs, conflicting evidence, and cases where the correct response is to stop or escalate. For each case, specify the input, expected outcome, acceptable alternatives, and observable failure conditions. Include examples drawn from real work when you can use them appropriately, and remove or protect sensitive information.

Run the same cases against a simple baseline, such as a fixed sequence or a single model call. Because model outputs can vary, repeat trials where practical and inspect the range of outcomes rather than treating one successful run as proof of reliability.

Measure the result and the path

Choose measures that match the task:

• Outcome quality: Are the answers correct, relevant, complete, and supported by the supplied evidence?
• Completion and handoff: How often does the workflow finish, return a useful partial result, or escalate appropriately?
• Decision quality: Did it select an appropriate branch and recognize ambiguity?
• Process quality: Were required checks performed, and did the run stop for the right reason?
• Operational cost: How many model calls or steps did the run use, and what were its elapsed time and cost?

Use direct checks for objective conditions, such as required fields or an exact calculation. For nuanced judgments, use a written rubric and human review; a model-based grader can assist, but compare its judgments with human assessments before relying on it. Review both false passes and false failures.

Inspect failures and rerun regression cases

Record enough of each run to reconstruct the task, intermediate outputs, checks, status, and final result. A trace may show that a poor brief came from an irrelevant source, a weak evidence check, or a drafting step that ignored a good source. Those are different problems and need different fixes.

After changing a prompt, model, check, or workflow path, rerun the same test cases alongside new ones. Track the measures that matter for the task and investigate regressions before widening the workflow’s use. A favorable average should not hide a recurring failure on an important edge case.

Where Does the Execution Layer Fit?

The workflow decides what task is being performed, which decisions the agent can make, what counts as success, and when to stop. An execution layer is a separate concern: it is where the application connects a workflow step to an external tool or API and receives the result. The workflow should interpret that result before deciding whether to continue.

A pre-launch tool/API router such as Relvo could sit in that execution layer. It is not a workflow builder, and choosing an execution layer does not replace workflow design or evaluation. For the product’s intended role, see Relvo's tool routing.

Conclusion

A useful agentic workflow gives an agent room to make decisions without leaving the outcome undefined. Specify the deliverable, divide the work, choose the decision points that need judgment, and make checks and stopping conditions part of the process from the start.

Pick one task your team can evaluate, build the simplest version that could complete it, and test it against ordinary and difficult cases. Add more autonomy only when those tests show which decision needs it and how you will tell whether it helped.

FAQ

What is an agentic workflow?

An agentic workflow is a process in which an AI agent makes some decisions about how to accomplish a task. The surrounding application can still define the goal, required checks, available paths, and stopping conditions.

How is an agentic workflow different from automation?

A conventional automated workflow follows predefined rules and transitions. An agentic workflow gives a model discretion over some choices, such as interpreting an ambiguous request or selecting the next research question, while fixed logic can still control other steps.

Does an agentic workflow need multiple agents?

No. One agent within a fixed process may be sufficient, and some tasks need no agent at all. Consider multiple agents only when dividing the work serves a clear purpose that you can test against a simpler design.

How do you know when an agentic workflow should stop?

Define success, incomplete-result, escalation, and resource-limit conditions before the workflow runs. The application should record which condition ended the run and avoid describing unfinished work as complete.

How do you measure whether an agentic workflow works?

Use representative test cases with explicit success criteria, then assess both the final result and the steps taken to produce it. Track task-specific quality, appropriate handoffs, recurring failures, time, and cost, and rerun the cases after changes.