Agentic workflows
A typical chat interaction is one request and one response, and then it's done, with a person deciding what happens next. An agentic workflow works differently: given a goal, the agent works out the sequence of steps needed to get there itself, deciding what to do next based on what the previous step actually returned, rather than a person specifying every step up front.
What makes that possible is a combination of tool use and a loop. The agent isn't limited to generating text, it can call a search, query an API, run code, or read a file, taking real actions rather than only describing what someone else should do. It then observes the result of that action, reasons about what it means, and decides the next step, repeating that cycle until the goal is met or it reaches a point where it needs to hand off to a person. The term overlaps heavily with AI workflow automation, which describes applying this same pattern to an existing business or operational process.
In a testing context, this looks like an agent that reads a bug report, decides which test to run based on what the report actually describes, executes it, reads the result, and decides whether to retry, escalate, or close it out, adjusting at each step instead of working down one fixed script regardless of what comes back. The tradeoff is honest: the more autonomy a workflow has, the more it needs monitoring, since a wrong decision early on can compound through every step that follows it.
What makes a workflow agentic
- The agent plans its own sequence of steps toward a goal, rather than following one a developer fully scripted in advance.
- It can call tools, like running a search, querying an API, or executing code, not just generate text.
- It observes the result of each step and decides what to do next based on that result.
- It can loop, retrying or adjusting its approach, until the goal is met or it reaches a point where it needs a person.
Where it shows up
- Reading a bug report, deciding which test to run based on what the report describes, and escalating only if the result looks like a genuine regression.
- Researching a topic across several sources, refining the search based on what earlier results turned up, before producing a final answer.
- Handling a multi-step customer request, like changing an order and then confirming the change by email, without a person handling each step.
- Debugging a failed build by reading the logs, forming a hypothesis, checking it, and revising if it turns out to be wrong.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Handles a multi-step task end to end, without a person having to hand-hold every individual step. | More autonomy also means more room to go wrong, a bad decision early in the workflow can compound through every step after it. See AI hallucination. |
| Adapts to what actually happens at each step, instead of following a fixed script that breaks the moment reality doesn't match it. | Debugging why an agent took one path over another takes more than reading a script, the reasoning sits inside the model. |
| Can call real tools and take real actions, not just describe what someone else should do. | Needs monitoring, and usually a clear handoff point to a person, rather than running fully unsupervised on anything consequential. |
Frequently asked questions
What's the difference between an agentic workflow and a chatbot?
A chatbot typically responds to one message at a time, with a person driving each turn of the conversation. An agentic workflow is given a goal and works through a sequence of steps on its own, deciding what to do next and calling tools along the way, with much less back-and-forth from a person at each step.
Is an agentic workflow the same as AI workflow automation?
They're closely related. AI workflow automation describes applying this idea to an existing business or operational process. Agentic workflow is more often used as the underlying technical term for the pattern itself, an agent planning and adjusting its own steps toward a goal, regardless of what kind of process it's applied to.
Does an agentic workflow need human oversight?
Usually, at least for anything with real consequences. Many teams start with a person reviewing the agent's decisions at key points, then gradually reduce that oversight for the parts of the workflow that prove reliable, while keeping a review step wherever a wrong call would be costly.
How Klarent helps
Klarent's AI agents work as an agentic workflow across the testing lifecycle, planning what to check, running it, reading the result, and deciding the next step, whether that's retrying, escalating, or moving on, instead of working down one fixed script regardless of what each step actually finds.