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Choose the least complex way to use an LLM that meets the task’s requirements: a provider chat interface for human-led work, one API call for a single response, a predefined workflow when the procedure can be specified, or an agent when the model must decide how to proceed through an open-ended sequence of actions.
This four-level ladder is a practical decision framework, not a formal industry taxonomy. Its most important distinction is between a workflow, where code controls the path, and an agent, where the model directs its next steps.
The four levels at a glance
| Level | Who directs the work? | Use it when |
|---|---|---|
| Provider chat interface | A person guides the interaction. | You want interactive assistance and a person can review or refine each response. |
| Single API call | Your application sends one task and uses the model’s response. | One model response is enough to complete the task. |
| Predefined workflow | Code controls the procedure and routes calls or tools along specified paths. | You can describe the steps or branches ahead of time, even if the model chooses among them. |
| Agent | The model dynamically selects actions and tools based on what happens during execution. | The number or sequence of steps is hard to predict in advance. |
The four levels are adapted from AI Advisor’s September 21, 2026 article on DEV Community. Moving down the list means delegating more control; it does not mean automatically getting a better result.
How to tell 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.” Its December 19, 2024 engineering article is a useful reference for the distinction.
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A practical test is to ask: before implementation, can you draw every path the execution can take? If the application’s code establishes the paths, the design is a workflow. The model may still classify a request, select a branch, or produce intermediate output; that does not by itself make the system an agent. If the model chooses what to do next based on observations from its environment, control is more agent-like.
This is a decision aid, not a strict boundary. A system can combine fixed stages with model-directed ones, and autonomy can sit on a continuum. Focus on who controls the route through the task, rather than whether a product or team labels a system an “agent.”
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Common workflow patterns
Anthropic describes several patterns for systems whose paths are defined in code:
- Prompt chaining: break a task into successive model calls, with one stage’s output feeding the next.
- Routing: classify an input and send it through the appropriate predefined path.
- Parallelization: run multiple calls at once, then combine or evaluate their outputs.
- Orchestrator-workers: have a coordinating component assign work to other calls or components within an arranged procedure.
- Evaluator-optimizer: generate an output, evaluate it, and use the evaluation in a defined refinement process.
These patterns can use model judgment within the steps while leaving the overall procedure under application control.
Choose the simplest level that meets the need
Start by identifying what the task actually requires, then stop at the first level that can meet it. If a person needs to direct and judge the result interactively, keep the work in chat. If one response suffices, a single API call avoids unnecessary orchestration. If the procedure is describable, use a workflow. Consider an agent when the next action depends on what the model discovers or what happens along the way.
Before choosing an agent, ask four questions. This is a practical checklist, not a validated scoring rubric:
- Is the task too complex to express as a procedure? If you can specify its paths, a workflow may be easier to control.
- Is the expected result worth the added time and cost? Dynamic tool use can add latency and cost; added autonomy is not free.
- Is the model capable at this type of work? A more autonomous loop does not make an unsuitable model reliable.
- With safeguards, is handing off still worthwhile? If the consequences are too high or the required safeguards erase the benefit, narrow the delegated task or retain human control.
Anthropic likewise recommends starting with the simplest solution that works. Its guidance notes that agentic systems may trade latency and cost for task performance, and that autonomy can produce higher costs and compounding errors. Both workflows and agents need testing; the difference is where execution-path control resides, not whether one can be tested and the other cannot.
Control the downside before adding autonomy
For a workflow, the predefined route can make it easier to specify and evaluate behavior in advance, but it does not guarantee that every input or failure has been anticipated. For an agent, observe what it does, limit the actions it can take, and define when it must stop. Anthropic recommends testing in sandboxed environments and adding suitable guardrails.
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When actions affect people, money, or external systems, useful safeguards can include restricting the agent’s scope, capping transaction amounts, requiring human approval for consequential actions, and preserving a rollback path where possible. These are design measures, not guarantees of safety. The appropriate controls depend on the action and its consequences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.If an agent is warranted, choose how to implement it
Once the task genuinely requires dynamic action selection, implementation choices differ in who owns the loop, who operates the runtime, what capabilities are packaged, and how much infrastructure the team must maintain. The following categories describe common approaches, not fixed product specifications.
| Approach | Who owns the loop? | Runtime and tools | What to weigh |
|---|---|---|---|
| Hand-written tool loop | Your application code controls request, tool execution, returned results, stopping conditions, errors, and logging. | Your system operates the execution environment and implements the tools. | Offers direct control, but requires you to build and maintain the loop and its operational handling. |
| Provider SDK tool runner | The SDK manages tool round trips; you implement tool bodies and application-specific handling. | You remain responsible for the tool execution environment. | Can reduce loop plumbing while leaving approval, error, and tool behavior to your application. |
| Agent SDK | An SDK packages more of the agent machinery. | Some task capabilities, such as reading files or running commands, may be provided as part of the SDK. | Check what it executes, what permissions it needs, and which controls remain yours to implement. |
| Managed service | The provider hosts execution and configuration. | The provider operates more of the runtime; exact included tools and context handling depend on the service. | May reduce infrastructure you operate, while making service capabilities, terms, and controls important selection criteria. |
Examples discussed in the source article include LangChain/LangGraph and Claude Agent SDK; those names are examples, not endorsements or guarantees of current features. Anthropic’s article notes that its tooling landscape has changed since its December 2024 publication. Before choosing a specific service or writing against an API, verify current documentation for supported tools, authentication, hosted execution, approval mechanisms, and terms. Anthropic also cautions that frameworks can obscure underlying behavior and recommends starting with direct API calls when that is the clearest way to understand the system.
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