AI agents vs workflows: why a proactive agent still needs a workflow

An AI workflow runs steps you defined in advance. An AI agent decides its own steps as it goes. Use a workflow when the job is known and repeats, and an agent when the path depends on what it finds. Most recurring work needs both: a workflow for the shape of the job, and an agent's judgment inside each step.

What is the difference between an AI agent and a workflow?

Anthropic's guide to building effective agents draws the line clearly. Workflows are "systems where LLMs and tools are orchestrated through predefined code paths." Agents are "systems where LLMs dynamically direct their own processes and tool usage."

	Workflow	Agent
Who decides the steps	You, in advance	The model, as it goes
Same input, same path	Yes	Not necessarily
Good at	Repeat jobs with a known shape	Open-ended jobs where the next step depends on the last
Easy to check	Yes, every run has the same structure	Harder, each run can differ
Breaks when	The real world drifts from the steps you wrote	The goal or the definition of done is vague

The same guide's advice on choosing: "workflows offer predictability and consistency for well-defined tasks," while "agents are the better option when flexibility and model-driven decision-making are needed at scale." And above both: "we recommend finding the simplest solution possible, and only increasing complexity when needed."

When should you use a workflow?

• The job repeats. A weekly report, a daily triage, a release note every Friday.
• The output has a fixed shape. Same sections, same file, same destination.
• Someone has to check it. A fixed shape is what makes a checklist possible.
• It runs unattended. On a schedule or an event, nobody is there to steer.

When should you use an AI agent?

• The path is unknown. Debugging, research, anything where step two depends on what step one found.
• Inputs vary a lot. Every customer email, every bug report, every account is different.
• Judgment is the work. Deciding which bug matters, which lead to chase, what a good reply sounds like.

Why does a proactive agent still need a workflow?

A proactive agent starts work on its own. That is useful, and on recurring work it brings three problems that a workflow solves:

1. Drift. Left to decide its own steps every time, an agent produces the Monday report in a slightly different shape every Monday. A workflow fixes the shape.
2. Nothing to check against. With no written definition of done, "finished" means only that the agent stopped. A workflow carries a checklist, so every run is held to the same bar.
3. Corrections that evaporate. A note given in a chat lasts as long as the chat. A correction to a workflow becomes a new version, and every later run follows it.

The agent keeps its judgment. The workflow decides what the job is, when it runs and what done means; the agent decides how to do each part well.

How do you combine agents and workflows?

Write the workflow as a brief, and let agents do the steps:

Part	What it holds	Example
Trigger	What starts it	Every weekday at 9:00, or when a pull request merges
Brief	The job in plain words: source, rule, limits, output	Pick the 3 front-page Hacker News posts most relevant to AI agents, skip anything older than 24 hours
Output	What one run produces, and where it goes	A 5-line TLDR in reports/hn-ai-tldr.md
Checklist	Facts a reviewer can verify on every run	The file exists, covers exactly 3 posts, each with its link
Approvals	What waits for a person	Posting the TLDR anywhere public

Inside each step, an agent reads, judges and writes. Around the steps, the workflow keeps every run the same shape and every result checked. Write a workflow in the Kavela docs walks through each part.

Do you have to choose between a workflow builder and an agent?

You don't. Drag-and-drop workflow builders make the shape explicit but leave judgment out, so every branch has to be drawn by hand. Chat agents bring judgment but no shape, so every run is a fresh improvisation. A workflow written in plain words sits between them: you describe the job the way you would to a colleague, the steps are drawn from your text, and an agent does each step with full judgment.

How does Kavela combine agents and workflows?

In Kavela, a workflow is a job written in plain words. Kavela draws the steps from your text, runs them with agents in your tools, and checks each run against the workflow's checklist.

• Agents do the steps. Kav, the lead agent, assigns each part to the agent best suited to it, and has a new one built when none fits.
• The workflow keeps the shape. It starts on a schedule, on an event from another service, when a file is added, or when you press Run.
• Every run is checked. A second agent checks the result against your checklist, line by line. Work that falls short goes back with the gap.
• Corrections stick. Describe a change in plain words and it becomes a new version with its own pass rate. Kavela compares two versions once each has 10 checked runs.
• Repeat jobs become workflows on their own. When you ask for the same job again a day or more after it was first done, Kav offers to save it as a workflow, with your corrections.

Proactive AI agents covers the other things a proactive agent needs alongside a workflow.

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