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An agent loop describes how an agent runs; a workflow graph describes the paths its work can take. They are complementary: a graph can contain a loop, while a loop alone does not show the full workflow. A compiler-like layer between a workflow description and its runtime is a useful architectural proposal, not a standard feature shared by all agent frameworks.
What an agent loop describes
An agent loop is a runtime cycle. A runner calls the current agent’s model, examines the response, performs any requested tool work or hands control to another agent, then continues. It returns when the model produces a final answer and no more tool work remains. The OpenAI Agents SDK documentation describes this sequence as an application-level turn.
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The loop is useful for model-led iteration: the model can respond to a tool result or other observation and choose what to do next. But the cycle alone does not define the system around it. The application still needs decisions about state, continuation, approvals, and which agent or component owns control at each point.
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A workflow graph makes steps and transitions visible. Nodes represent operations, such as calling a model or running a tool; edges represent possible paths between them. Conditions can determine which edge is followed, and some paths can return to an earlier node.
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LangGraph’s documented example uses an LLM node and a tool node. If the model response contains a tool call, the graph routes to the tool and then back to the LLM. If there is no tool call, the workflow ends. The graph is compiled before invocation, and the example makes the agent loop explicit as a path through the graph.
This resembles a compiler control-flow graph, where nodes and directed edges represent the possible flow of execution. The resemblance is conceptual: an agent workflow graph is not necessarily the same thing as a compiler’s control-flow graph, and the analogy does not imply that agent frameworks use a shared compiler design.
How loops and graphs work together
Think of the loop as the runtime engine’s repeated interaction with the model and tools, and the graph as a map of the broader workflow. The graph can show where the cycle starts, how a tool call or handoff changes the route, which conditions choose a branch, and where work stops. A graph can therefore represent a loop while also showing structure the loop description leaves implicit.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →The two views also clarify different kinds of decision ownership. A model may select its next action based on an observation; application code may route execution based on a deterministic condition; or the design may divide those responsibilities. Making that distinction explicit helps explain which decisions are model-led and which are enforced by the application.
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Handoffs and manager-led delegation
OpenAI’s orchestration guide distinguishes two multi-agent patterns. In a handoff, control moves to a specialist that takes over the conversation. In an agents-as-tools pattern, a manager invokes a specialist for bounded work and remains responsible for the final reply. When drawing a workflow, show who owns the user-facing answer after each branch; otherwise, the diagram may hide an important change in responsibility.
When to favor a loop-oriented or graph-oriented design
Neither representation is universally better. The useful choice depends on whether the workflow’s changing decisions or its explicit paths are more important to understand and control.
| Consideration | Loop-oriented emphasis | Graph-oriented emphasis |
|---|---|---|
| Control-flow visibility | Compact description of repeated model, observation, and tool interaction. | Branches, transitions, return paths, and stopping points are explicit. |
| Decision ownership | Can suit workflows where the model chooses the next action after each result. | Can make application-controlled routing and model-selected transitions easier to distinguish. |
| Changing workflow paths | Code-first orchestration can be easier to adapt when decisions are dynamic. | Explicit upfront branches can clarify known paths, but may add overhead as they multiply or change. |
| Inspection and control | May require examining runtime logic and traces to understand the overall route. | Offers a visible representation of the planned paths and conditions. |
OpenAI’s practical guide describes the tradeoff between graph-based approaches that define branches, loops, and conditions upfront and code-first approaches that express workflow logic in code. It presents this as a design tradeoff, not a benchmark proving that one approach is faster or more reliable. The right fit depends on the workflow and on how its routing needs to be inspected or changed.
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What a compiler-like layer could do
Here, “compiler” means a proposed boundary that translates a higher-level description of an agent workflow into a validated runtime plan. It does not refer to an established, universal agent compiler. A practical design could use these stages:
- Describe the intent. Specify the task, roles, allowed tools, expected outputs, and points requiring human approval.
- Validate the contracts. Check that steps define their inputs and outputs, tool permissions are declared, branches lead to valid destinations, and every path has a stop condition.
- Lower the description into an execution plan. Generate a graph, code-first orchestration, or another runtime representation. Keep model-selected transitions distinct from deterministic application routing.
- Execute and observe. Run the plan through the chosen runtime while retaining the state and traces needed to understand execution and support continuation.
- Evaluate changes. Compare intended behavior with observed runs before changing routes, tool access, or agent responsibilities.
This pipeline is a design proposal built from the ideas of graph compilation, control flow, and documented orchestration patterns. The cited materials do not prescribe these stages or establish a general-purpose compiler for agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask before choosing an architecture
- Can people inspect the important routes? Identify whether branches, transitions, and stopping points are visible enough for the workflow’s needs.
- Who chooses the next step? Separate model decisions from application-controlled routing.
- Who owns the response? Decide whether a specialist takes over after a handoff or a manager retains responsibility for the final answer.
- Where does state live? Plan how a run’s context is stored and continued. The OpenAI Agents SDK documentation lists application history, sessions, and conversation IDs as state strategies.
- What does explicit structure cost? Determine whether defining paths upfront improves clarity or becomes cumbersome as the workflow grows more dynamic.
- Which layer owns operations? In the SDK approach described by OpenAI, the application server owns deployment, tool implementations, state storage, and approval decisions. Make comparable ownership boundaries clear in any architecture.
How many agents should the workflow use?
Start with one agent unless a distinct role materially changes the instructions, tools, or policy. OpenAI’s orchestration guide recommends, “Start with one agent whenever you can.” A split can make sense when capabilities or policies need isolation, or when separate roles make traces easier to understand. Adding agents without a meaningful difference in responsibility creates more handoffs to design without necessarily clarifying the workflow.
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