Workflow vs Agent: Choosing Fixed Pipelines or Dynamic LLM Orchestration

Workflow vs Agent

A workflow is a system that arranges large language models (LLMs) and tools along a predefined code path , while an agent lets an LLM dynamically guide its own process and tool usage, retaining control over how the task is completed.

In plain language, a workflow is a fixed, rail‑bound process; an agent receives a goal and a toolbox, then decides on its own how to achieve the goal.

Key Comparison

Metaphor : Workflow = a train on tracks (stable, cannot deviate). Agent = a taxi driver (chooses routes, can detour).

Decision Maker : Workflow = developer writes the code. Agent = AI model decides in real time.

Path : Workflow = fixed, linear or preset branches. Agent = dynamic, looping, self‑correcting.

Advantages : Workflow = stable, fast, cheap. Agent = flexible, can solve complex unknown problems.

Typical Examples : Workflow = translation pipeline, content moderation. Agent = automatically writing code to resolve a GitHub issue.

Classic Workflow Patterns

Prompt‑Chain

This is the most basic pattern: a task is broken into an ordered series of steps, each LLM call feeding its output into the next. Developers can insert programmatic checks (“gates”) between steps to ensure correctness.

Mechanism : Each LLM output becomes the next LLM input.

Applicable Scenarios : Tasks that can be clearly decomposed into fixed subtasks; accuracy is prioritized over latency.

Example : Generate marketing copy, then translate it; or draft an outline, verify compliance, then write the full document.

Routing

The input is classified and routed to specialized downstream tasks.

Mechanism : Use an LLM or traditional classifier to separate concerns and build dedicated prompts.

Applicable Scenarios : When the task contains distinct categories that are best handled separately.

Example : Direct general queries, refund requests, and technical support tickets to different flows; route simple questions to a cheap model (Claude Haiku) and complex ones to a stronger model (Claude Sonnet) to balance cost and performance.

Parallelization

Multiple LLM calls run concurrently, and a program aggregates the results.

Sectioning : Split the task into independent subtasks that can run in parallel (e.g., one model handles the user query while another checks content compliance).

Voting : Run the same task several times with different prompts and vote on the best output (e.g., multiple code‑vulnerability scans to improve coverage).

Applicable Scenarios : When subtasks can be parallelized for speed or when diverse attempts increase confidence.

Orchestrator‑Workers

A central LLM (the orchestrator) dynamically decomposes the task, delegates pieces to worker LLMs, and then synthesizes their results.

Mechanism : The orchestrator decides which sub‑tasks are needed based on the specific input.

Applicable Scenarios : Complex tasks where the required subtasks cannot be predicted in advance.

Example : Multi‑file code refactoring or a search task that gathers information from many sources before answering.

Evaluator‑Optimizer

A loop that mimics human iterative improvement: one LLM generates a response, another evaluates it, and the process repeats until a quality threshold is met.

Mechanism : Generation‑evaluation cycles continue until standards are satisfied.

Applicable Scenarios : Situations with clear evaluation criteria where iterative refinement adds clear value.

Example : Literary translation with an evaluator LLM providing nuanced criticism, or a complex search where the evaluator decides whether further probing is needed.

From Theory to Practice

Many platforms support workflows, such as Kuzi, Dify, and n8n. The article demonstrates a concrete Kuzi workflow using the Prompt‑Chain pattern.

Steps (illustrated with screenshots):

Upload a knowledge base containing the reference material.

Add nodes in the order: Input → Knowledge‑base query → LLM processing → Output.

Configure the knowledge‑base node: set maximum recall, minimum similarity, enable query rewriting and re‑ranking.

Connect the output of the knowledge‑base node to the LLM node, which composes a response based on the retrieved information.

Run the workflow, test the knowledge‑base node individually or the whole pipeline, and observe the final answer.

The screenshots (kept as

tags with only the src attribute) show the UI actions: creating the workflow, dragging the knowledge‑base node, linking it, setting parameters, adding the LLM node, and executing the query.

The author concludes that hands‑on experimentation is essential because the variety of nodes and tricks cannot be fully learned from a single example.