The Simple Definition
AI workflow: a fixed sequence of steps. Step 1, then step 2, then step 3. Each step may use AI, but the path is predetermined by a human.
AI agent: a goal-driven system. You give it an objective. It decides which tools to use, in what order, and when the work is complete.
Workflows are predictable. Agents are flexible. Workflows are auditable. Agents are harder to reason about.
AI Workflow Example
Step 1: new lead arrives in CRM.
Step 2: LLM reads the lead's company website and summarises it.
Step 3: LLM scores the lead against your ICP criteria.
Step 4: if score > 80, LLM drafts an outreach email.
Step 5: email goes to a human for approval.
This is a workflow. The sequence is fixed. The AI is used in specific steps, but a human designed the path.
AI Agent Example
You tell the agent: 'Qualify this lead and start the outreach process.'
The agent decides: first, look up the company on LinkedIn. Next, read their blog. Then check if they are hiring. Based on what it finds, the agent decides whether to draft an email, add the lead to a nurture campaign, or flag it as unqualified.
Different leads get different paths. The agent picks the path each time. A human did not predefine the steps.
When to Use a Workflow
The process is well understood and repeatable.
Auditability matters. You need to prove to a regulator or customer exactly what happened.
Mistakes are expensive. You cannot afford the agent picking a strange path.
You want predictable costs. Workflows have predictable LLM call counts.
When to Use an Agent
The task is open-ended. You do not know in advance what the right path is.
You want to handle long-tail situations that would require too many workflow branches.
You have time to monitor and tune the agent's behaviour.
You accept occasional surprises in exchange for flexibility.
Most Real Products are Hybrid
Good systems blend workflows and agents. A human-designed workflow handles the common cases. When the workflow hits an edge case, it hands control to an agent.
This is how mature AI systems are built: workflows for reliability, agents for flexibility. Pure-agent systems are often too unpredictable. Pure-workflow systems are often too rigid.
Most platforms (n8n, Gumloop, Lindy, Relay) support both patterns. Pick a tool that lets you mix them.
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Explore Agent Console HQFrequently Asked Questions
ChatGPT's conversational interface is not an agent by itself. ChatGPT with tools enabled (browsing, code interpreter, custom GPTs) behaves more like an agent because it decides which tool to use.
Zapier is a traditional workflow tool that has added AI features. Its AI steps are usually a single LLM call embedded in a larger workflow.
It depends on the use case. For internal tooling and research, yes. For customer-facing interactions without human review, mostly not yet. Always test extensively.
Workflows. Agents make more LLM calls because they have to plan, decide, and verify. A workflow with the same end result typically costs less in tokens.
Yes. Think of agents as a step inside a workflow. The workflow calls the agent when it needs flexible decision-making, then the workflow resumes.
Agentic AI is the broader category of AI systems that take actions in the world, not just generate text. Agents are the primary example of agentic AI.
Probably not yet. Most businesses get 80% of the value from well-designed workflows. Start there. Add agents only when your workflow branches get out of hand.