The Tool Desk
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What orchestration decides
Orchestration determines what happens next in an LLM application: which step, tool, or agent runs; whether the workflow branches or repeats; and who remains responsible for the result. A chain is one simple form of orchestration: one step’s output feeds a predetermined next step. More flexible workflows may combine application-controlled routing with model-directed decisions. OpenAI describes both approaches and allows them to be mixed in its orchestration guidance; AWS describes agentic workflows that coordinate multiple steps and adapt to intermediate results in its AWS patterns guide.
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The key architectural question is not “How many agents can we use?” It is “Which decisions should be explicit application logic, and which benefit from a model interpreting context?” More flexibility brings more control-flow, state, integration, approval, and observability work. The documentation describes capabilities and design patterns, not a universal performance ranking.
Which workflow shape fits the task?
| Approach | How the next step is chosen | Good fit | Main design consideration |
|---|---|---|---|
| Fixed prompt chain | The application follows a predetermined sequence, passing each output to the next step. | A known, stable process whose route does not need to vary. | Simple to reason about, but not suited to routes that depend on interpreting intermediate results. |
| Code-controlled workflow | Application logic selects branches and invokes tools or agents. | Branches, side effects, and business rules should remain explicit and reviewable. | Make the conditions and transitions clear in code or the chosen workflow representation. |
| Model-directed orchestration | A model interprets the request or an observation and chooses a useful next action. | Open-ended work where the right next step cannot be fully specified as a fixed sequence. | Keep consequential operations behind defined tool contracts and application controls; do not assign every business rule to a planner. |
| Graph-based workflow | Nodes represent steps, while edges express transitions, branches, or loops; the application or model can determine the route as designed. | A workflow whose conditional paths and tool loops should be visible as a graph. | A graph makes the structure explicit but does not by itself decide which decisions belong to code versus a model. |
These are design shapes, not mutually exclusive products. A graph can contain ordinary code, model calls, and tools; an application can use a fixed chain in one part and a model-directed loop in another. LangGraph’s documentation, for example, shows a conditional path from an LLM call to a tool node and back, or onward to completion: Workflows and agents.
Keep fixed sequences fixed
If step B always follows step A, adding an autonomous agent to decide whether B should run creates a decision point without an evident need. Use a chain or code-controlled flow for stable sequences, especially when the steps trigger consequential side effects or encode business policy.
Use model decisions for real uncertainty
Model-directed routing is useful when the next action depends on understanding a request, an intermediate result, or an observation from a tool. Define the actions available to the model and the boundaries around them; let application logic retain control over operations that must meet explicit business or safety rules.
How should specialists be coordinated?
Delegation has two distinct ownership models. In a handoff, control transfers to a specialist, which takes over the current branch. In a manager-with-specialists design, a central agent calls specialists for bounded work, receives their results, and remains responsible for synthesis and the final response. OpenAI documents both patterns in its orchestration and handoffs guide.
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| Pattern | Who owns the current branch? | Use it when | Design question |
|---|---|---|---|
| Handoff | The selected specialist takes over. | Choosing the specialist is part of the task and that specialist should continue the interaction or branch. | What context and responsibility must transfer for the specialist to continue correctly? |
| Manager calls a specialist as a tool | The manager retains responsibility; the specialist returns bounded work. | A central agent should combine outputs, such as summaries or classifications, into one result. | What precise input and output contract lets the manager use the specialist’s result? |
Add a specialist for a distinct responsibility
A separate agent is justified when it brings a genuinely different capability, instructions, tool set, policy boundary, or responsibility—or makes execution traces easier to understand. Splitting a workflow without such a distinction adds more prompts, traces, and approval surfaces to manage. OpenAI’s official orchestration guide puts the default plainly: “Start with one agent whenever you can.” Treat that as a design principle, not as a measured guarantee that one-agent systems outperform multi-agent systems.
What state must survive between steps?
For work that may pause, resume, retry, or move between agents, decide what must persist at each transition. At a minimum, consider the context needed to continue the task, intermediate outputs, execution status, and information needed to determine whether the next action is safe. The right state model depends on the workflow; an agent’s conversation history alone should not be assumed to capture every operational fact.
- Task context: the request and relevant information a later step needs.
- Intermediate results: outputs that should be reused rather than regenerated or lost during a transition.
- Status: where execution stands and whether a step is pending, complete, or requires recovery.
- Continuation requirements: what is needed to resume safely after a pause, error, or handoff.
AWS’s agentic workflow guidance discusses execution-state tracking, intermediate results, and retries. For its AWS implementation patterns, it names Amazon DynamoDB, Amazon S3, and Amazon RDS as possible stores, alongside services such as AWS Step Functions, EventBridge, Lambda, and Amazon Bedrock. These are examples within AWS architectures, not a prescription for other stacks or every workflow: AWS Prescriptive Guidance: Agentic AI patterns and workflows.
Design recovery as part of the flow
Specify what should happen when a step fails or returns an unusable result: whether to retry, route to another step, pause, or stop for review. Record enough state to distinguish work that has completed from work that needs another attempt, especially where repeating an action could have a side effect. Recovery behavior is an application design responsibility; the existence of an orchestration framework does not establish that a particular workflow is safely recoverable.
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Who should own the runtime?
Runtime choice determines how much execution responsibility sits with a managed service, an SDK inside your application, or your own integration code. OpenAI’s Agents documentation distinguishes the Agents API for long-running tasks with OpenAI-managed progress, the Agents SDK for applications that control the agent loop, and the Responses API for lower-level integration. The same documentation compares runtime location, state handling, tool execution, and integration effort; verify current behavior and availability in the live documentation before building against a specific option.
| OpenAI option | Documented positioning | Responsibility to evaluate |
|---|---|---|
| Agents API | Long-running tasks with OpenAI-managed progress. | Whether the managed runtime and its state and tool handling fit the application’s control and integration needs. |
| Agents SDK | Applications that control the agent loop. | Whether the application is prepared to own the loop and its execution responsibilities. |
| Responses API | Lower-level integration. | Whether the additional integration responsibility is appropriate for the desired control. |
This comparison describes documented positioning, not a performance, reliability, or cost ranking. AWS’s named services likewise illustrate one ecosystem’s implementation choices rather than a neutral framework comparison.
How to choose and review an orchestration design
Make the design defensible by writing down the control and ownership decisions before choosing a framework. Compare candidate designs across these axes:
- Control flow: Are routes explicit in code or a declared graph, or can the model choose among actions?
- Result ownership: Does a specialist take over through a handoff, or does a manager retain responsibility and synthesize bounded results?
- State and recovery: What survives transitions, and what should happen on pause, failure, or retry?
- Runtime responsibility: Is progress managed by a service, controlled by an in-application SDK, or handled through lower-level application integration?
- Integration and execution environment: What tool connectivity, hosting, sandboxing, and application-side work does the design require?
- Reviewability: Can operators understand tool calls, handoffs, approvals, and state changes from the workflow’s traces?
Prefer the least complex design that meets the task’s needs. Start with a fixed chain when the route is fixed; keep branches explicit when they encode policy or side effects; introduce model-directed routing, specialists, or retained state only where the task requires them. OpenAI, LangChain, and AWS documentation describes ways to build these capabilities, but the cited material does not provide a head-to-head benchmark, independent reliability comparison, or total-cost model. Evaluate a candidate against your own workflow requirements rather than treating framework capability as proof of superiority.
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