Workflow vs Agent
Also: workflows vs agents · orchestrated workflow · autonomous agent
Workflows follow predefined code paths; agents let the model dynamically direct its own steps and tool use.
Why CCAR-P tests this
CCAR-P tests choosing the least-autonomous pattern that solves the problem, since unneeded agency adds cost, latency, and failure modes.
A workflow orchestrates LLM calls and tools along a predefined path written in code — prompt chaining, routing, parallelization. The control flow is fixed; the model fills in content at known points. This is predictable, testable, and cheap.
An agent hands control flow to the model: it decides which tools to call and in what order inside an agentic loop, adapting to results you couldn't script ahead of time. This buys flexibility for open-ended tasks but costs determinism.
The design rule is start simple. Use a workflow when the steps are known and stable; reach for an agent only when the task genuinely requires runtime decisions the code can't enumerate. Many 'agent' requirements are actually a routing or chaining workflow in disguise. When in doubt, prefer the more constrained pattern and add autonomy only where it earns its keep.
Exam trap
Picking the agent 'because it's more capable' — the exam rewards the simplest sufficient pattern, and defaulting to full autonomy where a fixed workflow would do is the wrong answer.
Commonly confused with
Related concepts
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