Traditional automation works well when every step can be specified in advance: when X happens, do Y. Many knowledge-work problems are messier. The relevant evidence changes, exceptions are common, and the next step depends on what the system discovers. Agentic workflows are designed for that uncertainty.
How humans and AI work together
What is an agentic workflow?
An agentic workflow is a sequence of work in which one or more AI agents can decide how to pursue a goal, choose tools, take actions, evaluate intermediate results, and adapt their next steps within defined limits. It combines some AI autonomy with workflow structure, permissions, context, observability, and human checkpoints.
In one sentence
An agentic workflow can reason about what should happen next instead of following only a fixed script.
Why it matters
How it differs from adjacent ideas
- A fixed automation follows a predetermined path.
- An AI-assisted workflow uses a model inside one or more fixed steps.
- An agentic workflow allows the model to choose or revise parts of the path.
- A fully autonomous system acts with minimal human intervention; most business workflows should not assume this is the goal.
Original example
A company wants to prepare for a quarterly business review. A fixed workflow could pull standard reports into a slide template. An agentic workflow can notice that retention declined in one segment, investigate support themes and usage patterns, compare the segment with previous quarters, ask a finance tool for margin implications, and recommend that the meeting focus on onboarding quality rather than top-line pipeline. The path emerges from the evidence.
What people get wrong
Autonomy is not the same as reliability. Giving an agent more freedom can expand what it handles, but it also expands the space in which it can drift or fail. Anthropic recommends starting with simple, composable patterns and adding complexity only when it measurably improves results.
Scott’s take
The most exciting agentic workflow is not the one with the most agents. It is the one that removes the most distance between a meaningful signal and a well-judged business response. Complexity should earn its keep.
Evidence and further reading
Related terms
- AI agentA chatbot answers; an agent acts.
- Human-in-the-loopLet machines handle scale and repetition while people own judgment and consequence.
- AI orchestrationOrchestration turns a collection of AI capabilities into a working team.
- Revenue orchestrationRevenue orchestration turns scattered customer activity into coordinated commercial action.
Established concept
Written by Scott Salkin, a founder, operator, and former B2B software CMO. Published August 4, 2026. Last reviewed August 4, 2026.