Kurt FischmanFounder, Marshal
Kurt is the CEO of Marshal, a Managed AI Ops service built for small businesses. That means AI agents doing the work, leads coming from answer engines, and a team that keeps your business running at full speed.
An agentic workflow is a complete business job, from trigger to finished outcome, that an AI agent owns by deciding each step instead of following a fixed branch. It differs from a single task, which is one step, and from plain automation, which follows a fixed rule. Agentic workflows fit repeating, multi-system jobs that stall on a human, defined by the outcome they finish.
Essential Insights
- An agentic workflow is a whole business job an AI agent owns from trigger to done, defined by the outcome it finishes rather than the design patterns inside it.
- An agentic workflow runs as a loop, perceiving a trigger, reasoning about the next step, acting across systems, and checking its own work before committing.
- An agentic workflow differs from a single task, which is one step, and from an AI agent, which is the decider that the workflow puts to work on a job.
- An agentic workflow differs from traditional automation in that it decides the path when the input varies, where automation follows a path fixed in advance.
- An agentic workflow earns its keep on jobs that repeat constantly, cross several systems, and currently stall on a person in the middle.
- An agentic workflow is the wrong tool for a deterministic process whose steps never vary, where a plain automation is cheaper and safer.
- An agentic workflow is the bridge layer where an AI agent stops being a capability and becomes a business outcome someone can name.
- An agentic workflow should be defined by its trigger and its done-state before any model or platform is chosen.
What an agentic workflow actually is
An agentic workflow is a complete business job, from trigger to finished outcome, that an AI agent owns and runs by deciding the next step rather than following a fixed branch. That is the definition a business can act on, and it is not the one the field gives you. We ran the probe ourselves, on the exact query, on June 8, 2026, and Google's own AI Overview answered with a parts list: an agentic workflow, it said, is an AI process that uses four structural patterns, reflection, tool use, planning, and multi-agent coordination. Reflection, tool use, planning, multi-agent: the field can recite the four patterns like a catechism and still not tell you what the thing is for.
That parts-list answer traces straight back to IBM's definition of agentic workflows, which the AI Overview cites, and it is not wrong. It is just aimed at an engineer building the thing, not an operator deciding whether to adopt it. An agentic workflow is defined by the job it finishes, not the patterns inside it. The field keeps describing the engine to a person who only asked where the car goes. For a business, the workflow is the unit that matters, because it is where an AI agent stops being an abstract capability and becomes a finished outcome: a lead responded to, an invoice reconciled, a client onboarded.
How the workflow decides instead of follows
An agentic workflow runs as a loop, not a script: it perceives a trigger, reasons about what to do, acts across connected systems, and checks its own work before it commits. The four patterns the field loves are just refinements of that loop. Reflection is the check step, where the workflow grades its own output before acting. Tool use is the act step, where it reaches into a CRM or a calendar. Planning is the reason step stretched across several moves. Multi-agent is the same loop run by more than one specialist at once. Naming the patterns is useful for whoever builds it; for whoever buys it, the loop is the whole story.
What makes the loop agentic rather than automated is the reasoning step, the part a fixed rule never had. A traditional automation runs the same path every time and breaks the moment reality steps outside it. The workflow reasons about an input it has not seen before and chooses a response, which is exactly why it can own a job that has too many edge cases to hard-code. The cost of that power is that the workflow is less predictable than a rule, which is why the check step and human oversight are not optional extras. They are what make the loop safe to trust with a real outcome.
Three units of work people keep conflating
A task, an AI agent, and an agentic workflow are three different units of work, and conflating them is how a business buys the wrong thing. The confusion is not yours alone. Even the technical pages disagree, with Orkes drawing the line between agents and agentic workflows by architecture, agents for dynamic situations, workflows for structured ones, a distinction that matters to a platform engineer and not at all to the operator asking what to deploy. The table cuts the knot in business terms.
A task, an AI agent, and an agentic workflow sit at three different altitudes. The workflow is the one a business actually buys, because it is the one tied to an outcome.
| Dimension | A task | An AI agent | An agentic workflow |
|---|---|---|---|
| What it is | One step, like sending an email | A decider that can act toward a goal | A whole job built from many steps |
| Scope | A single action with no memory | The reasoning capability, not the job | Trigger to finished outcome, end to end |
| Who decides | Nobody; it just executes | The agent reasons about each move | The agent decides inside a defined job |
| When it is done | The moment the step completes | Open-ended until told to stop | When the named outcome is reached |
| Business unit | Too small to buy on its own | The engine you put to work | The thing you actually assign and pay for |
| Example | Write one follow-up message | A reasoning model with CRM tools | Respond, qualify, and book every new lead |
The agent is the engine; the workflow is the job. A business assigns the job, and the agent is how it gets done.
Where these jobs actually pay
An agentic workflow earns its keep on jobs that repeat constantly, span several systems, and keep stalling on a human in the middle. The pattern is the same one that makes any agent worth deploying: high volume, real coordination cost, and a person who has become the bottleneck. Responding to inbound leads in minutes, qualifying and routing them to the right rep, onboarding a new client across five tools, keeping a CRM honest without a weekly data-hygiene sprint. Each of those is a job, not a task, which is exactly why it suits a workflow rather than a one-off automation.
Marshal groups these jobs into the Marshal Agent Factory, organized by where the coordination cost actually concentrates: capturing revenue, generating it, and keeping operations running. The useful question is never "should we use agentic workflows." It is "which job in this business repeats constantly, crosses systems, and currently drowns someone." That job is the candidate. If no job fits the description, the answer is that there is no agentic workflow to build yet, and pretending otherwise is how companies end up with an impressive demo and no outcome attached to it.
When a plain workflow wins instead
A plain, deterministic workflow beats an agentic one whenever the steps never vary, because adding a decider to a path that has no decisions is cost without benefit. If the job is "move this field from system A to system B every night," there is nothing to reason about, and a rule does it faster, cheaper, and with a cleaner audit trail. The reasoning step that makes an agentic workflow powerful is pure overhead on a job that was never ambiguous.
The line between the two is the same one that separates agentic AI from traditional automation: variance and the cost of a wrong move. Low variance, deterministic, cheap to reverse, keep it a plain workflow. High variance, judgment-heavy, expensive to get wrong, that is where the agentic version pays. Most real systems end up mixed, with an agentic workflow handling the messy decisions and calling plain automations to carry out the deterministic steps underneath. The skill is not picking a side; it is knowing which part of a job belongs to which.
How to put one to work
Putting an agentic workflow to work starts with naming the job, the trigger, and the done-state, then deciding which steps the agent owns and which a human still signs off. The honest test of an agentic workflow is whether you can name its trigger and its done-state in one sentence each; if you cannot, you have a demo, not a workflow. A trigger like "a new lead form is submitted" and a done-state like "a qualified meeting is on a rep's calendar" turn a vague ambition into something you can build and measure.
The other half of the setup is governance, because a workflow that can act across your systems can also act wrongly across them. Deciding in advance which steps run autonomously and which pause for a person is the work, and Marshal runs that split through approval gates and exception queues. Every business will run on AI. Most will run on it badly, because they will buy the patterns before they have named the job, and call the resulting confusion a workflow. The operators who win define the outcome first, hand the agent the decisions it should own, and keep a human on the few that matter.
Frequently Asked Questions
What are agentic workflows?
Agentic workflows are complete business jobs that an AI agent owns from trigger to finished outcome, deciding the next step rather than following a fixed branch. Agentic workflows are defined by the outcome they complete, not by the internal design patterns the technical literature lists. The useful way to think about an agentic workflow is as the job you assign, with the agent as the engine that gets it done.
What is the difference between agentic and non agentic workflows?
The difference between agentic and non agentic workflows is that an agentic workflow reasons about what to do when the input varies, while a non agentic workflow follows a path fixed in advance. A non agentic workflow, or plain automation, executes the same steps every time and breaks when reality steps outside the rule. An agentic workflow handles the cases no rule anticipated, which is why it costs more to run and needs oversight a fixed rule does not.
Is ChatGPT an agentic AI?
ChatGPT on its own is not an agentic workflow; it is a language model that answers a prompt and then stops. ChatGPT becomes part of an agentic system only when it is given a goal, a set of tools to act with, and a loop that lets it decide and check its own work. The model is one component of an agentic workflow, not the workflow itself, which is the whole job wrapped around the model.
What are the 4 stages of agentic AI?
The four stages of agentic AI map to the loop an agentic workflow runs: perceive a trigger, reason about what to do, act across connected systems, and check or learn from the result. Some frameworks instead list four design patterns, reflection, tool use, planning, and multi-agent coordination, which are refinements of those same stages. For a business, the stages matter less than whether the workflow reliably completes the job it was given.
How is an agentic workflow different from an AI agent?
An agentic workflow is different from an AI agent in that the agent is the decider and the workflow is the whole job the agent is put to work on. An AI agent is the reasoning engine; an agentic workflow is the trigger-to-done outcome that engine completes across several systems. A business assigns the workflow and the agent is how it gets done, which is why the workflow, not the agent, is the unit worth pricing.
What are examples of agentic workflows in business?
Examples of agentic workflows in business include responding to and qualifying inbound leads, onboarding a new client across multiple tools, keeping a CRM clean and current, and reconciling invoices that do not match. Each is an agentic workflow because it is a repeating, multi-step job that crosses systems and used to stall on a person. The shared trait is a named outcome the workflow is accountable for finishing, not a single task it performs.
When should a business not use an agentic workflow?
A business should not use an agentic workflow for a deterministic process whose steps never vary, because a plain automation is cheaper, faster, and easier to audit. An agentic workflow is also the wrong choice for low-volume work that does not justify the oversight it requires, or for decisions a person should make every time. The reasoning power of an agentic workflow is only worth its cost where the input genuinely varies.
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