Effective AI agent workflows start with a clear decision about control: which steps should follow predefined code paths, and which decisions should the model make? Define the task and its boundaries first, then design how context persists, integrate the necessary tools, constrain permitted behavior, and evaluate the complete execution.
Orchestration means coordinating those model calls and tool operations. Anthropic distinguishes workflows, where code defines the execution paths, from more autonomous systems. OpenAI describes agents as systems that accomplish tasks on a user’s behalf using LLMs for workflow execution. That distinction matters because building an agent changes who makes decisions during the task: the design must account for the model’s role in handling complexity. OpenAI’s practical guide provides that framing.
Core architectural patterns: composable workflows and frameworks
Anthropic’s guidance favors simple, composable patterns over unnecessary framework complexity. A useful starting point is to separate the execution pattern from the software used to implement it. A framework is an implementation choice; it does not answer how much control the model should have.
| Approach | Where execution control sits | Design question |
|---|---|---|
| Predefined workflow | Code orchestrates LLMs and tools through defined paths | Can the task’s execution paths be specified in advance? |
| Agent-directed execution | The LLM participates in deciding how to accomplish the user’s task | Which decisions require the model to handle workflow complexity? |
The practical recommendation is to keep routing deterministic wherever the task permits it and identify each decision that needs model judgment. This makes the choice of autonomy explicit. It also gives a team a clearer basis for discussing whether a proposed agent adds useful decision-making or merely duplicates a known sequence.
Anthropic’s engineering guide supports the emphasis on composability. Its reported experience should not be read as proof that frameworks are always unsuitable, or that adding agents necessarily improves a workflow.
Step 1: Define goals, inputs, and workflow boundaries
Write down the task the system must accomplish before selecting its orchestration software. OpenAI’s description of agents centers on accomplishing tasks for users; translate that into a concrete workflow specification.
Use these questions to make the specification reviewable:
- What result constitutes completion?
- What inputs will the workflow receive, and what information does it need to proceed?
- Which steps can follow predefined code paths?
- Which decisions will the model make?
- What falls outside the system’s permitted task?
These are design recommendations, rather than a prescribed implementation. Their purpose is to connect the user’s goal to the control structure. If the team cannot explain why a decision belongs to the model, revisit that part of the workflow before expanding its autonomy.
Keep the task boundary distinct from the policy boundary. The former defines what the workflow is trying to accomplish. The latter, addressed in Step 4, defines what it is allowed to do while accomplishing it.
Step 2: Design state management and context persistence
OpenAI’s guidance identifies clear state management and context persistence as requirements for effective agent workflows. Address them in the design, alongside execution control.
For a stateful interaction, specify what information should remain available as the task progresses. A practical design review should ask:
- Which information describes the task’s current progress?
- Which context does the next model call or tool operation need?
- What information must persist across interactions?
- Where does responsibility for updating that information sit?
Use the answers to document what each step receives and what it passes onward. This is a way to make the state contract explicit, rather than leaving context persistence as an unspecified property of the chosen framework.
The high-level requirement does not establish a particular memory structure, storage backend, or state-passing API. Select those implementation details separately and validate them against the workflow’s needs. A design that names its required context is ready for that evaluation; a design that merely asks for “memory” still leaves the central requirement unresolved.
Step 3: Integrate tools and choose a framework
Tools are part of the execution structure: in Anthropic’s definition of a workflow, code orchestrates both LLMs and tools. Review tool integration in terms of the task and the decisions already assigned to the model.
For each proposed tool, document its role in accomplishing the task, the context it needs, and where its use belongs in the execution path. These recommendations connect integration to the preceding design work instead of making tool availability the organizing principle.
The framework examples identified in Anthropic’s guidance include Claude Agent SDK and Strands Agents SDK. Their inclusion provides names to consider; it does not establish a market-wide standard, a ranking, or a feature-by-feature comparison.
Evaluate a framework against the design you have written: can you express the intended control paths, make state responsibilities clear, and inspect execution? Those are selection questions derived from the architecture, state, and observability requirements. Keep implementation complexity proportionate to the task.
Step 4: Implement guardrails and validate permitted behavior
Reco’s guardrails guide describes policy guardrails as boundaries for acceptable agent behavior, data access, and decision-making. It presents guardrails as a way to support predictable, safe, and compliant operations.
Turn those categories into explicit requirements. Define what data the workflow may access and which decisions it may make, then review the proposed execution paths against those limits. A useful validation question is whether the workflow can recognize and handle a request that crosses a defined boundary.
For testing, derive cases from the policy: assess permitted behavior and attempts to exceed the stated limits. This is an editorial recommendation for making guardrails reviewable, rather than a claim about a particular enforcement mechanism.
A guardrail’s stated purpose does not prove its effectiveness. Reco’s guidance is not, by itself, evidence that a particular deployment meets its compliance obligations. The design and its validation must support any claim made about the deployed system.
Step 5: Test, debug, and monitor agent performance
Hugging Face’s agent observability and evaluation lesson describes tracking agent actions, tool usage, model calls, and responses to improve performance. It identifies latency, costs, request errors, and user feedback as key monitoring metrics.
Use that execution record to examine how the workflow reached a result. When debugging, review the actions, calls, and tool use associated with the response. For evaluation, connect the result to the completion criteria established in Step 1 and the permitted behavior established in Step 4.
AI agent workflow evaluation checklist
The following checklist translates those observability categories into review questions. It proposes an evaluation routine without prescribing unsupported numerical thresholds.
- Execution visibility: Can you inspect the agent’s actions, tool usage, model calls, and responses?
- Task completion: Does the result satisfy the workflow’s defined goal?
- Latency: What latency does the workflow show, and is it acceptable for the intended task?
- Costs: What costs does execution incur, and do they fit the workflow’s requirements?
- Request errors: Which requests fail, and what does the execution record reveal about them?
- User feedback: What do users report about the results?
- Boundary validation: Do evaluated executions respect the specified data-access and decision-making limits?
Review these dimensions together. A recommendation to optimize latency or cost should remain tied to the task’s required result; user feedback and request errors provide additional grounds for deciding what needs attention.
Conclusion: Make the workflow reviewable
Before committing to an implementation, assemble a design that a reviewer can follow: the user’s goal, the execution paths, the model’s decision responsibilities, the context each step requires, the permitted behavior, and the evaluation questions. That document gives architecture, integration, and testing a shared reference.
The remaining work is deployment-specific. The cited guidance establishes the need for state persistence and policy boundaries, but it does not supply a verified state-management implementation or demonstrate guardrail effectiveness for your application. Treat those as requirements to validate, rather than capabilities to assume.
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