What is an agentic workflow? Complete definition and operating principles
An agentic workflow isn't 'smarter automation', it's a system that puts decision-making in the hands of AI. This breaks down how it works in four steps, perception, reasoning, action, and feedback, and explains why that final feedback loop is what separates a demonstration from something that actually works in production.
By
Tenten AI 研究團隊
應用 AI
Published
March 17, 2026
Read time
5 分鐘
If you had to answer "What is an agentic workflow?" in one sentence: An agentic workflow is a system that lets AI handle tasks on its own, it observes what's happening, plans what to do next, uses tools to execute those plans, then checks the results and adjusts course if needed, repeating this cycle until the task is complete.
The crucial difference is autonomy. Traditional automation requires you to specify each step in advance, do A, then B. An agentic workflow means you set a goal and let the system figure out how to reach it.
What is an agentic workflow? How does it differ from traditional automation?
Traditional RPA or if-this-then-that scripts are like railroad tracks laid down in advance. When an unexpected event occurs, they stop or fail. An agentic workflow is like a driver following a map: when traffic appears, it takes a different route; if a road is closed, it replans the journey. The real difference isn't whether you're using AI. It's about who makes the decisions.
That's why it's not just a feature. It's an integrated system that operates as a continuous cycle.
How it works: perception, reasoning, action, feedback
At its core, it's a four-step process that repeats itself.
| Stage | What the System Does | Corresponding Technology | Without This Step |
|---|---|---|---|
| Perception | Understands the task, fetches current data and environment state | RAG, API queries, document parsing | Stale data means decisions fail from the start |
| Reasoning | Breaks down the goal, plans paths, picks the next step | LLM reasoning, task planning, routing | Rigid logic that crashes on exceptions |
| Action | Invokes tools to actually execute | Function calling, tool/API calls | Just suggestions, never actually does anything |
| Feedback | Checks results, judges success, corrects and reruns | Result validation, reflection, human review | Errors go unseen, cascade throughout |
Perception is the system understanding the current situation, grasping the task, retrieving relevant data from knowledge bases or APIs, and noting what's happening now. This typically relies on RAG and API queries. Perception quality determines what becomes possible afterward: if you give it outdated inventory data from three months ago, it will produce outdated answers regardless of the model's capabilities.
Reasoning means taking that information to break down the goal, map possible paths forward, and determine the next step. This is where LLM reasoning proves useful, and where agentic workflows differ most from standard automation. Rather than following a predetermined if-else chain, the system evaluates multiple options and selects the best course.
Action is where reasoning translates into work. The system executes function calls, queries APIs, and runs tools to carry out its decisions: sending emails, updating orders, creating reports. Many demonstrations stop at the recommendation stage and never continue. Without this step, the system only advises, it never actually performs anything.
Feedback means checking whether the execution worked and whether the goal was achieved. If not, the system corrects the approach and tries again. This transforms the workflow from a single attempt into a loop that self-corrects, and this is what makes it agentic.
Why feedback is often skipped
Most problems we've encountered occur at step four.
Perception, reasoning, and action are achievable for most teams. A working demonstration doesn't require much. The challenge is feedback, getting the system to detect when it failed and run the cycle again.
A manufacturing client deployed a procurement agent. In testing, it worked perfectly: read the order, compared prices, submitted it without problems. Two weeks after launch, we found that approximately 11% of orders had incorrect amounts. The system wasn't aware of these errors because no feedback loop was in place to validate results. After adding result validation and a human review step, the error rate dropped below 0.5%. The system didn't become more intelligent. It simply began checking its own work.
Whether an agent succeeds in production depends not on the demonstration, but on how it handles its mistakes.
At Tenten, we integrate feedback into the architecture from the start, not as an optional add-on. Attractive demonstrations don't prove viability. What matters is production: real users relying on the system, data cycling back to improve performance. That's when you have a functioning autonomous workflow.
One stuck workflow
is enough to begin
Tell us what the team does today, where it breaks down, and what a better working day should look like.