## Flexibility and Autonomy

Perhaps the starkest contrast between workflows and agents lies in their level of **autonomy** and flexibility in execution.

A **workflow system has limited autonomy**: the LLM follows the path laid out for it by the code. If a workflow has, say, three steps (analysis → transformation → verification), the LLM will be invoked three times in that order and *cannot deviate*. The only flexibility comes from branching that the developer explicitly coded (for example, an `if/else` that chooses one of two sub-flows based on the LLM’s answer to a classification question). Even such branching is a predefined decision point. Essentially, workflows handle variability **by enumeration** – the developer anticipates possible variations of the task and encodes how each should be handled. Anything outside those anticipated variations is likely to cause the workflow to fail or produce suboptimal results. This makes workflows very **controlled**: they won’t do something radically unexpected because they simply *can’t*. On the flip side, they might be brittle if an input requires a new kind of handling that wasn’t built in.

An **agent offers far greater autonomy**: it has the capacity to handle situations it was not explicitly programmed for, by making on-the-spot decisions. Given a broad goal, an agent can figure out how to achieve it (within the limits of its toolset and knowledge). Anthropic describes that agents are suited for problems *“where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path”*. Because the agent decides how many steps to take and which actions to perform, it can flexibly expand or adjust the plan as needed. For example, if a research agent finds too much information from a first search, it could decide to summarize and then do a follow-up search for specific details – a capability that a rigid workflow might not have unless explicitly built for that scenario. This autonomy makes agents powerful but also introduces **uncertainty**. The agent might take an approach the developer didn’t expect, which could be ingenious… or erroneous.

The **degree of autonomy** also implies differences in how errors or novel situations are handled. A workflow, lacking autonomy, will typically just fail or stop when encountering an unforeseen situation (unless the developer included a generic fallback). An agent, by contrast, might recognize an unexpected result and attempt to recover – e.g., if a tool’s response is nonsensical, the agent could try a different tool or rephrase the query. Modern LLM agents have shown ability to **recover from errors or dead-ends** by revising their plans. However, autonomy can also lead to **compounding errors** – if the agent makes a bad decision early on, it might build on that flawed state with further actions. Anthropic cautions that an agent’s autonomous nature means higher risk of such compounding errors and thus warrants careful use of guardrails and iteration limits.

In practical terms, choosing more autonomy (an agent) means **ceding some control to the AI** in exchange for flexibility. If the application domain is well-understood and mistakes are unacceptable, high autonomy is likely unnecessary and even risky – a workflow will suffice and be easier to trust. But if the domain is complex, with many edge cases or unknown unknowns (e.g. an agent that has to solve arbitrary user requests on a computer), the flexibility of an autonomous agent becomes necessary. Developers then must mitigate the risks through rigorous testing and by **trust calibration** – initially use the agent in constrained or sandboxed environments until it proves it can be trusted with minimal supervision. Over time, as models improve and demonstrate reliable reasoning, the envelope of safe autonomy can be expanded.