Agentic Workflow
An agentic workflow is an automated process where an AI decides what step to take next based on context, instead of following a fixed sequence.
Traditional workflows are deterministic graphs: do A, then B, then C. Agentic workflows give the AI a goal and a toolbox and let it pick the path. The AI may loop, branch, retry, escalate, or call a sub-agent — whatever the situation requires. The trade-off is predictability for capability. A deterministic workflow is easier to debug but only handles cases its author imagined. An agentic workflow handles novel cases but produces a wider range of outcomes — which is why production deployments wrap them in approval gates, guardrails, and observability.
Example
Refund request comes in. A deterministic workflow: parse → check policy → approve or deny. An agentic workflow: read the email, decide if it's a refund request or something else, look up the order, check policy, check loyalty status, draft a reply, escalate if amount > $200, otherwise send.
How OpenLabor uses it
Every OpenLabor mission is an agentic workflow. The employee picks the path; the platform records every step for review.
Are agentic workflows the same as autonomous agents?
Closely related. An autonomous agent is the actor; an agentic workflow is the pattern of execution. You can have an agentic workflow with a human-in-the-loop at key checkpoints.
What's the failure mode?
Loops, hallucinated steps, and runaway tool calls. The fix is bounded retries, max-step limits, and approval gates on irreversible actions.
Related: mission, run, tool-use, approval-gate.
AI Labor Glossary