Over the past five years no-code platforms have changed who gets to build software. Webflow, Retool, Zapier, Appsmith and Bubble each put a different spin on the same idea: design software around workflows rather than code. For people who want more control, Pipedream blends workflows with custom code on top of a large library of integrations.
That shift let non-developers build tools for their own needs. It also raises the next question: once workflows are the backbone of how software is designed, what happens when you hand them to an AI system?
That is what we mean by an agentic flow: a workflow where the key components are managed autonomously by AI. To make the term precise, first break a workflow into its parts.
Five parts of every workflow
Most workflows, in no-code tools or traditional systems, reduce to five elements. We shorten them to T.T.C.A.D:
- Trigger event: the action that starts the workflow. A customer makes a bank deposit.
- Task: the job to be done. Check that the account is active; record the transaction.
- Condition: the rule that has to be evaluated. Is the deposit over $10,000? Is the account blacklisted?
- Action: what gets executed based on the condition. Accept the deposit, flag it, or report it as fraudulent.
- Data: the information the workflow runs on. The depositor’s name, the amount, the recipient account number.
Run the bank example end to end: a client deposits money (trigger), the system checks the amount and the account status against its rules (task and condition), then accepts or flags the deposit (action), handling identity and account details throughout (data). The same five parts underlie operational workflows from banking to e-commerce, and they are what tools like Zapier and Bubble are built around.
What makes a flow agentic
A flow becomes agentic when an AI system takes full control of three of the five: tasks, conditions and actions. Humans still define the trigger and supply the data. The AI does not assist with the core logic; it owns it.
- Tasks: the AI carries out the steps, such as verifying account status or analysing transaction patterns, without a person in the loop.
- Conditions: the AI evaluates the rules and can adapt them as it learns, for example adjusting fraud-detection thresholds as new patterns emerge.
- Actions: the AI decides and executes the outcome: approve, flag, or escalate to a human.
The agentic version of the bank example: the system watches deposits (trigger) using customer and transaction details (data). It checks for fraud indicators on its own (task), judges whether the deposit matches suspicious criteria it has learned over time (condition), and approves or flags it (action). Automation becomes decision-making, with the process refining itself instead of waiting for someone to rewrite the rules.
Why it matters
No-code platforms let users design workflows. Agentic flows let AI run them, with an adaptability that static rules cannot match. Zapier’s AI features, such as suggested workflow steps, hint at this; an agentic flow goes past suggestions to full execution.
How we use this
This is how we scope AI integration work at Tecmie. For each workflow a client brings us, we decide which of the task, condition and action the model should own, and what stays with people. Two of our products are the framework applied in different places: Wootiv runs agentic pipelines for small and medium businesses that need to automate processes and client interactions, and Workforce applies the same split to software delivery, automating scoping, delegation and quality assurance so a non-technical manager can run a build.
Hand tasks, conditions and actions to AI and you get systems that evolve with the problems they solve. The open question, and the one worth arguing about, is where human oversight and ethical constraints sit in that split.
Adapted from How to design an Agentic workflow?, first published on andrewmiracle.com.