AI Agent Interview FAQ: Distinguishing Agents from Workflows and Their Design Trade‑offs

Core distinction

The essential difference between a Workflow and an Agent is who holds execution control.

Workflow : The large model and tools are orchestrated along a pre‑written code path. Decision points are fixed by the programmer; the model only performs a specific sub‑task.

Agent : The large model dynamically plans the task flow, decides which tools to use, and controls how the task is completed. Decision‑making resides with the model.

The distinction is not about intelligence or tool count, but about the ownership of control.

Why the distinction matters

Workflow paths are fixed, providing predictability and stable results, which makes them suitable for production environments. Agents have non‑fixed paths, offering flexibility at the cost of higher compute, higher latency, and potential error amplification. Choosing the wrong approach can waste resources and reduce stability.

Common Workflow patterns (as summarized by Anthropic)

Prompt Chaining : Split a task into fixed steps, feeding the output of one step to the next (e.g., write copy → translate). Suitable when the task can be decomposed into deterministic sub‑steps.

Routing : Classify input and dispatch it to dedicated downstream prompts or tools (e.g., customer‑service queries routed to refund, technical support, or general inquiry flows). Simple cases use cheap models; complex cases invoke larger models.

Parallelization : Run multiple sub‑tasks simultaneously or run the same task with different models and aggregate results (e.g., content moderation with separate models for response generation and policy checking).

Orchestrator‑Workers : A central model breaks the task into subtasks, assigns them to worker models, then aggregates the outcomes. Subtasks are decided at runtime, blurring the line with Agents.

Evaluator‑Optimizer loop : One model generates output, another evaluates it, and the process repeats until a quality threshold is met. Works for tasks with clear evaluation metrics, such as literary translation.

Agent execution process

Receive a task (instruction or dialogue).

When the task is clear, the Agent plans and executes it autonomously.

At each step it gathers relevant information from the environment (tool results, code execution feedback) to decide the next action.

If a blockage or a predefined checkpoint is encountered, the Agent seeks human confirmation.

The process stops when the task is completed or when a maximum number of iterations is reached to avoid infinite loops.

Because Agents are autonomous, tool design is critical. Suitable scenarios include open‑ended problems where the number of steps cannot be predetermined and where the model’s judgment is trusted (e.g., programming agents that modify multiple files to resolve a GitHub issue, or computer‑use agents that operate a desktop).

Decision guide: when to use Workflow vs. Agent

If the task definition is clear and steps can be cleanly broken down → use a Workflow for stability and control.

If the task is open, step count is unpredictable, and the path cannot be hard‑coded → consider an Agent for flexibility.

Many situations require only a single model call plus retrieval and a few examples; no Workflow or Agent is needed.

Common misconceptions

Confusing “more intelligent” or “more autonomous” with the deciding factor; the true criterion is control ownership.

Viewing Agents and Workflows as mutually exclusive; they are different forms within an agentic system and are often combined, with orchestrator‑workers serving as a hybrid.

Anthropic’s guidance emphasizes building the simplest system that meets the need: add complexity only when it demonstrably improves results.