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What are the four levels of using an LLM?
Think of these as a practical design ladder, not a formally standardized taxonomy. Each level gives the model a different amount of control over the work.
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- Provider chat interface: A person interacts with ChatGPT, Claude, Gemini, or another provider’s interface, guiding the task and judging the responses. This is a sound choice when a person should remain involved throughout.
- Single API call: An application sends one request to a model and uses its response. Choose this when the task can be handled in one call without a separately controlled sequence of model and tool steps.
- Predefined workflow: Application code owns the procedure, directing model calls and tools along paths specified in advance. The model may classify an input, select a branch, or produce intermediate output without controlling the overall route.
- Agent: The model dynamically selects actions and tools in a loop, using results from the environment to decide what to do next. This can fit tasks whose sequence or number of steps is hard to predict beforehand.
The ladder describes control, not quality. A well-designed chat interaction or one-call application can be more appropriate than an agent if a person or a simple request already meets the requirements.
How do you distinguish a workflow from an agent?
Anthropic defines the distinction this way: “Workflows are systems where LLMs and tools are orchestrated through predefined code paths. Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.” (Anthropic, “Building effective agents,” December 19, 2024.)
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A practical question follows: before building the system, can you draw every path the execution might take? If the application’s code lays out the route and the model operates within it, you are describing a workflow. If the model’s observations and choices determine the next steps while the task is underway, you are describing an agent. Treat this as a decision aid, not a strict boundary: a system can combine fixed stages with a model-directed segment.
A model can choose a branch in a workflow
Dynamic output does not by itself make a system an agent. A workflow can ask a model to classify a request and route it to one of several predefined branches; the application still owns the procedure. Common workflow patterns include prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer designs, as described by Anthropic.
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When should you choose each level?
Stay in chat when a person should drive
Use a provider’s interface when the user needs to clarify the request, review intermediate answers, or decide what to ask next. Keeping a human in the loop is not a failed attempt at automation; it may be the intended design.
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If the application needs a single generation, extraction, or other response and no multi-step procedure is required, a direct call avoids building orchestration that adds no necessary capability.
Use a workflow when the procedure is specifiable
Choose code-defined orchestration when the task has known stages, branches, or checks. Keeping the route in code makes the procedure easier to inspect and evaluate in advance, though it does not remove the need to test the model’s outputs or the whole system.
Use an agent only when the task needs dynamic direction
An agent is worth considering when the model must decide which action to take next based on what it discovers, and that sequence cannot be usefully laid out as a fixed procedure. Before handing over that control, ask:
- Is the task genuinely too difficult to express as a procedure?
- Is the likely result worth the added time and cost?
- Is the model capable at this kind of work?
- Would delegation still be worthwhile with appropriate limits and human review?
These are judgment checks, not a validated scoring rubric. If one points against autonomy, simplify the design or narrow what the model is allowed to handle.
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What changes when you give an agent more control?
Agentic systems may trade latency and cost for task performance, and allowing a model to direct a longer sequence can raise costs or let errors compound. Anthropic advises using the simplest solution that works, testing in sandboxed environments, and adding appropriate guardrails (“Building effective agents,” December 19, 2024). Those controls reduce exposure; they do not guarantee correct or safe outcomes.
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For actions with external consequences, design safeguards around the specific action rather than assuming a general-purpose agent is safe. Examples include limiting the scope of permitted actions, capping transaction amounts, requiring human approval for consequential steps, and preserving a rollback path where possible. Workflows also require testing: a fixed route is easier to specify, but model behavior and integrations can still fail.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.If an agent is justified, how should you implement it?
Implementation styles differ mainly in who owns the action loop, who operates the runtime, what machinery is packaged, and how the team handles approvals, failures, and infrastructure. The options below describe broad approaches, not fixed product specifications.
| Approach | Who owns the loop? | Runtime and packaged support | Best fit |
|---|---|---|---|
| Hand-written tool loop | Your application controls requests, tool results, stopping conditions, error handling, and logging. | Your team operates the execution environment and implements tool behavior. | When direct control and inspectability matter and the team is prepared to build and maintain the orchestration. |
| Provider SDK tool runner | The SDK can manage tool-call round trips; your code supplies tool implementations and can add approval or failure handling. | Some loop mechanics are packaged, but the application still owns the tool bodies and their safeguards. | When the provider’s runner fits the use case and you want less loop code without handing over tool implementation. |
| Agent SDK | A packaged agent framework handles more of the orchestration. | May include machinery for tasks such as reading files or running commands; exact capabilities depend on the current SDK. | When the provided task environment and abstractions fit, and their behavior is understood and tested. |
| Managed service | The provider hosts more of the execution and configuration. | Can reduce infrastructure your team must operate; the extent of hosted support depends on the service. | When reducing runtime operations is valuable and the service’s controls, terms, and capabilities meet requirements. |
Examples associated with these categories include LangChain/LangGraph and Claude Agent SDK, but names and features can change. Anthropic’s December 2024 article explicitly cautions that its tooling landscape has changed since publication. Check current vendor documentation for APIs, hosted features, authentication, and terms before choosing a product. Anthropic also recommends starting with direct API calls and warns that frameworks can obscure the underlying behavior (Anthropic, “Building effective agents”).
Quick Recap
How to make the decision
- Write down the task and its required outcome. Identify where human judgment, external actions, or intermediate checks are needed.
- Choose the smallest control surface that fits. Start with chat or one call; move to a workflow if the application must coordinate known stages.
- Check whether the route can be specified. If code can define the steps and branches, retain control in a workflow. Consider an agent only if the model must direct an open-ended sequence based on observations.
- Evaluate the added downside. Account for cost, latency, testing, failure handling, and consequences of incorrect actions; add proportionate limits and human approval.
- For an agent, choose who operates what. Decide how much loop logic, runtime infrastructure, tool support, and approval handling your team wants to own, then verify current product details.
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