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What does an agent loop describe?
A loop describes runtime behavior: the sequence that repeats while an agent works. In OpenAI’s documented SDK flow, a runner calls the current agent’s model, inspects the result, executes requested tool work or switches agents after a handoff, and continues. It returns when the model produces a final answer with no remaining tool work.
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This makes the loop a natural fit for model-led iteration. The model can respond to a tool result or other observation and choose what to do next. But a loop alone does not specify the whole system. It does not, by itself, settle where state is stored, how work resumes, whether approval is required, or whether control belongs to a manager or a specialist.
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A graph represents workflow structure as nodes and directed transitions. Nodes can stand for operations such as calling a model or running a tool; edges describe which step can follow and under what condition.
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LangGraph’s documented example shows how a graph can encode an agent loop: an LLM node routes to a tool node when the model returns a tool call, and the tool node returns to the LLM. If there is no tool call, the workflow ends. The graph is compiled before it is invoked.
This is not the same thing as saying an agent workflow graph is a compiler control-flow graph. In compiler terminology, a control-flow graph represents basic blocks and the possible paths between them. That concept helps explain why explicit nodes and edges can support inspection and analysis, but it does not make the two kinds of graphs identical.
How do loops and graphs work together?
Use the loop to reason about the runtime engine’s repeated interaction with the model and tools. Use the graph to reason about the wider workflow: which operations exist, where branches occur, who takes responsibility after a handoff, and where execution can end. The graph can show the loop as one path through a larger system.
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These views answer different questions, so neither is a universal replacement for the other:
| Design question | Loop view | Graph view |
|---|---|---|
| What happens at runtime? | Shows the repeated model, result-inspection, and tool-or-handoff cycle. | Shows the nodes and routes through which that cycle proceeds. |
| Where is a decision made? | Highlights cases where the model chooses what to do after an observation. | Makes application-defined routes and conditional transitions visible. |
| Can work repeat? | Repetition is the central runtime pattern. | A return edge can represent repetition alongside branches and stopping points. |
| What is easier to inspect? | The runtime cycle is concise, but it does not necessarily display the whole workflow. | Branches and transitions are explicit, though defining them upfront can add work as workflows become more dynamic. |
The right balance depends on who should choose the next action. The model may make a decision, application code may route deterministically, or the workflow may divide control between them. Making that division explicit is more useful than arguing that one representation is always better.
Who owns the answer when agents collaborate?
OpenAI’s orchestration guide distinguishes two multi-agent patterns by control and response ownership:
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- Handoff: control moves to a specialist, which takes over the conversation.
- Agent as a tool: a manager invokes a specialist as a bounded capability and remains responsible for the final response.
The practical question is: who owns the user-facing answer at each branch? That decision affects how a workflow should represent transitions and which agent is expected to finish the task.
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What could a compiler between intent and execution do?
Here, “compiler” is an architectural analogy: a layer that takes a higher-level workflow description, checks it, and translates it into a form a runtime can execute. It is not an established, universal agent architecture. Existing documentation shows graph compilation and discusses code-first versus declarative workflow approaches, but does not prescribe one general-purpose agent compiler.
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A plausible design for such a layer would be:
- Describe intent: declare the task, roles, permitted tools, expected outputs, and points where human approval is required.
- Validate contracts: check that steps have clear inputs and outputs, tool permissions are declared, and branches lead to valid destinations with defined stop conditions.
- Lower to an execution plan: produce a graph, code-first orchestration, or another runtime representation. Keep model-selected transitions distinct from deterministic application routes, and preserve loop conditions.
- Run and observe: execute the plan through the chosen runtime while retaining the state and traces needed for debugging or resumption.
- Evaluate changes: compare intended behavior with observed runs before changing routes or agent roles.
This pipeline is a design proposal, not a set of prescribed stages from a framework. Its value would be the boundary it creates: workflow intent can be checked before runtime execution, while the runtime remains responsible for carrying out the plan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is explicit graph structure worth the effort?
OpenAI’s practical guide describes a tradeoff: requiring every branch, loop, and condition to be defined upfront can improve visual clarity, but may become cumbersome as workflows grow more dynamic and complex. It contrasts graph-based approaches with expressing workflows in code. Treat this as a documented tradeoff, not a performance ranking of all graph and code-first frameworks.
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For an architecture discussion, compare concrete properties of the workflow rather than looking for a universal winner:
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- Control-flow visibility: Can a reviewer see the branches, transitions, and stopping points that matter?
- Decision ownership: Is the next action selected by the model, application code, or both?
- Response ownership: Does a specialist take over, or does a manager retain responsibility for the final reply?
- State and continuation: Where does state live, and how can work continue across turns or workers? OpenAI’s SDK guide describes application history, sessions, and conversation IDs as state strategies.
- Change cost: Does defining routes upfront make this workflow easier to understand, or create overhead as its branches change?
- Operational boundaries: Which layer owns deployment, tool implementations, state storage, approval decisions, tracing, and evaluation?
What belongs in the application, not just the workflow diagram?
In OpenAI’s SDK approach, the application server owns deployment, tool implementations, state storage, and approval decisions. A diagram can show where those responsibilities affect the path, but it does not implement them. The design should therefore make clear which responsibilities belong to the runtime and which remain with the surrounding application.
There is no established quantitative basis here for claiming that loops or graphs are universally faster, cheaper, more reliable, or more productive. Choose a representation for the control and visibility the workflow needs, then assess its behavior in the actual application rather than inferring performance from the architecture label.
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