Workflows vs Agents

The load-bearing architectural distinction inside "agentic systems." Both use LLMs and tools; the difference is who holds control of the process:

Workflows — LLMs and tools orchestrated through predefined code paths. The developer fixes the structure; the LLM fills the slots. Predictable and consistent, best for well-defined tasks.
Agents — systems where the LLM dynamically directs its own process and tool usage, maintaining control over how it accomplishes the task. Flexible and model-driven, best when you can't predict the required steps or hardcode a path.

An agent is typically just an LLM using tools in a loop against environmental feedback: it begins from a human command or discussion, plans, operates independently, gains "ground truth" from the environment at each step (tool results, code execution) to assess progress, and pauses for human feedback at checkpoints or blockers. Because it can run many turns autonomously, it carries higher cost and the risk of compounding errors — hence stopping conditions (e.g. a max-iteration cap), extensive testing in sandboxed environments, and guardrails. Autonomy suits scaling tasks in trusted environments.

The decision ladder: for many apps, a single optimized LLM call is enough; when more is warranted, prefer a workflow for predictability on well-defined tasks and an agent when flexibility and model-driven decisions at scale are needed. See Agentic Simplicity — sometimes the right answer is not to build an agentic system at all.

Claims

Workflows orchestrate LLMs/tools through predefined code paths; agents let the LLM dynamically direct its own process. principle — durable: this control-locus distinction is the stable axis for reasoning about agentic architectures.
Prefer workflows for predictability on well-defined tasks; prefer agents when flexibility and model-driven decision-making at scale are needed. best practice — context: you've already decided some agentic complexity is warranted; the choice hinges on whether the task's steps are predictable.
Use agents for open-ended problems where you can't predict the step count or hardcode a path — and where you can trust the model's decisions. best practice — context: selecting full autonomy specifically; trust and unpredictability are the gating conditions.
Agent autonomy brings higher cost and compounding-error risk — bound it with stopping conditions, sandboxed testing, and guardrails. best practice — context: operating autonomous agents; the mitigations are contingent on running in a not-fully-trusted or high-stakes setting.
An agent is fundamentally an LLM using tools in a loop on environmental feedback. observation — the implementation is usually straightforward even when the task is sophisticated.
Give the agent "ground truth" from the environment at each step to assess progress. principle — durable: without feedback from real tool/execution results an autonomous loop cannot self-correct.
A bounded loop requires a finite run boundary and explicit stop/hand-back conditions supplied before acting. best practice — context: operating iterative agent loops; an independent source (Forward Future's Loopy) corroborates the stopping-condition discipline — loops "are not permission to run forever." See Agent Loop.
The more autonomy an agent has, the more the binding constraint becomes human oversight bandwidth — reviewing and steering the work, not producing it. principle — durable: a second witness (Nimbalyst, an IDE built around fleet oversight) confirms the human-in-the-loop checkpoint as a first-class concern; see Agent Supervision.

Agentic Simplicity — the prior question: how much complexity, if any, is warranted.
Agentic Workflow Patterns — the five concrete workflow shapes that sit between a single call and a full agent.
The Augmented LLM — the unit both workflows and agents are composed from.
Agent-Computer Interface (ACI) — an agent is only as reliable as the tools it loops on; ACI quality gates agent quality.
Agent Loop — the bounded, checked, hand-back-capable loop as a reusable artifact.
Agent Supervision — the human→agent side: how you keep control of autonomous agents once you're running many of them.
Distillate: Building Effective Agents.
Distillate: Forward-Future/loopy — a catalog and skill for bounded, reusable agent loops.
Distillate: Nimbalyst — Visual Workbench for Supervising Coding Agents.

Workflows vs Agents

Claims

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