7 Steps to Creating the Perfect Agentic Workflow

In this article, you’ll learn exactly how to build an agentic workflow; one that automates tasks with minimal human intervention while ensuring reliability and scalability.

My name is Dom Steil, and I’m the Founder and CEO of StateSet. Over the past decade, I’ve been building software and developing AI agents. My experience includes early work on botbuilder, botkit, and Microsoft’s LUIS (Language Understanding Intelligence Service) back in 2015–2016. Today, I specialize in creating AI agents powered by the latest generative models, focusing on workflow automation, reasoning agents, and computer-use agents.

I’ve deployed AI agents at scale that handle thousands of monthly transactions completely autonomously, powering some of the fastest-growing online businesses worldwide. Below, I’ll share the seven essential steps for creating a robust, reliable, and perfect agentic workflow.

1. The Request

Every workflow begins with an input; be it an email, a customer service ticket, or an order from Shopify. This incoming data, once parsed, is provided to the agent. Ensuring you capture and format these inputs properly is crucial for the rest of the process.

2. The Filters

Not every request should trigger a response or action. Set up filters to decide whether a given request is worth processing. For instance, you might filter based on:

Type of request (create vs. update)

Email subject or sender

Channel (e.g., a specific chat system)

Presence of certain tags or intents

By intelligently filtering requests, you avoid unnecessary or irrelevant agent actions.

3. The Workflow

Next, you establish a deterministic workflow comprising multiple steps. This involves:

1. Parsing the input from the previous step.

2. Retrieving additional data from relevant systems of record.

This phase serves as your “data prep,” assigning variables your workflow will use. Being systematic here guarantees you have all the context you need further down the line.

4. The Context

Context is everything. At this stage, you:

Pull in relevant knowledge-base data, rules, attributes, or examples.

Leverage VDB (Vector Database) retrieval and other data sources to provide the agent with a “perfect but finite” memory.

Optionally use memory systems (like mem0) to recall previous user–agent interactions.

Consolidate all of this into your system prompt or the first item in the chat history array, ensuring the model has every piece of information it needs.

5. The Call

This is where you actually call your model (e.g., a GPT-based system) with:

A list of functions the agent can invoke.

The chat history (including the system prompt).

Intent detection and activity execution logic.

Here, the agent:

1. Processes the request using the context provided.

2. Potentially makes API calls to gather more data or create external changes.

3. Returns structured responses (e.g., status, activity name, reasoning logs).

Because this is the heart of your workflow, it requires a high degree of accuracy and determinism. Ideally, you capture logs for auditing and debugging, then generate a follow-up message based on the results.

6. The Review

After the model responds, review and validate that response. This could involve:

Escalation rules (e.g., “If confidence < 0.8, escalate to a human”).

Guardrails to ensure no harmful or incorrect information is sent out.

Formatting or additional data transformation to get the output into a final, user-friendly state.

7. The Response

Finally, send your response back to the user or downstream system. This can include:

Posting a message to a chat or help desk system.

Sending a confirmation via email or SMS.

Logging completion metrics to maintain a record of the workflow.

You can introduce a scheduler here if you need to delay responses. Once the response is successfully delivered, the workflow execution is complete.

Key Takeaway

These seven steps repeat for every interaction with your agent. If any step fails, no response is sent, ensuring your system preserves a high degree of confidence in every interaction.

If you’re building AI Agents and want to learn more about creating robust, agentic workflows, let’s connect: dom@stateset.com