Recently I showed a friend the AI workflow I’ve been building. He stared at the screen and asked, “Wait… do you code?”
No, I don’t. But I do know how to use AI really well – well enough to build systems that adapt to me and solve my needs.
Over the past few weeks, I built a multi-agent workflow that emails me five Substack Notes inspirations every morning at 8 a.m., tailored to my style and focus areas. I built this system for two reasons: 1) to give myself a daily reminder to post consistently, and 2) to get fresh perspectives from my AI agents that can either support or challenge my own thoughts on various issues. Sometimes I'll adapt and explore one of its suggested angles or use an article it surfaces as a starting point. Other times, the ideas simply nudge me in a different direction, and I end up writing a note from scratch. In every case, this workflow helps me move faster and with more consistency.
My workflow orchestrates five different agents:
Agent 1 analyzes my long-form writing style and content (Substack Posts),
Agent 2 pulls fresh AI and finance news every day,
Agent 3 combines the output from the two to generate idea inspirations,
Agent 4 packages the options and sends me an email each morning,
and Agent 5 logs the option & action I decide to pursue in a spreadsheet.
Designing and wiring these agents together took plenty of trial, error, and iteration. After weeks of building, debugging, and refining, I now have a fully functioning workflow that’s become an indispensable part of my everyday routine.
Here’s a quick demo of how the workflow actually works in practice. I’ve set it so that the workflow is auto-triggered every morning at 8 am, but for this video I manually triggered it mid-day so you can see how it works in real time.
First, What is an Agentic Workflow?
When it comes to modern-day AI, for many people, especially non-technical beginners, AI simply means a smart chatbot like ChatGPT that spits out answers to questions.
In reality, AI has many subfields: machine learning, computer vision, natural language processing, robotics, and knowledge representation and reasoning, among others. These are the research domains that drive progress.
But when you move from the research lab to the real world, it’s more useful to think in terms of functional categories. These categories cut across subfields and describe how we actually experience AI day-to-day.
Among these categories, two dominate today: deterministic automation and generative AI.
Deterministic automation refers to rule-driven workflows that execute steps (if x → then y), fetch data, log outputs, and enforce rules. Think of it as the rails and infrastructure that keep things moving.
Generative AI refers to systems that create new text, images, audio, or code. Think of it as the creative engine that makes new content possible. These are powerful but probabilistic, so they need guardrails.
The most interesting frontier is the convergence of the two, and in its most advanced form (as we think of it today), becomes agentic AI. These are systems at the intersection of creation+guardrails+execution that can plan/route actions, use tools, and leverage memory/state to adapt over time, typically with a human-in-the-loop for oversight.
So, What Did I Actually Build?
My multi-agent workflow relies on five agents: two handle generative work (style analysis and idea synthesis) and three handle deterministic automation (news sourcing, email delivery, and choice logging). Together, they form a reliable daily system that ships five tailored ideas at 8 a.m. without fail. I call this agentic-lite: a working proof-of-concept that shows how generative AI and deterministic automation can be combined into a structured workflow that runs on schedule, produces consistent outputs, and builds the foundation for learning over time.
Pairing generative AI with deterministic automation only works when roles and handoffs are explicit: who creates, who packages, who remembers, and when the human steps in. Each of my agents has one specific job, and I, as the human-in-the-loop, review, refine, or reject the outputs as the last checkpoint.
Why go multi-agent instead of asking one agent to do everything? Because splitting work across multiple agents is often more reliable and cost-effective than forcing one giant agent to do it all.
When you use multiple AI agents, you can break down complex problems into focused units of work or knowledge, and each one becomes easier to handle. Microsoft frames the advantages of using multi-agents as specialization, scalability, maintainability, and optimization. In practice, I’ve seen all four in my own system: each agent stays focused on one task, I can add or swap agents without touching the rest, debugging is localized and simple, and I can choose the cheapest method or most efficient model for each job.
The principle is similar to how organizations are structured. You would never expect one person to handle product development, engineering, finance, marketing, sales, and customer success all at once. Teams are divided by function, and there is usually a central leader (often a product manager) who aligns them on vision, timelines, and process so the product actually ships.
My system works the same way: the agents each own a task, the workflow provides the rails, and I serve as the central coordinator who reviews the output, performs quality control and ensures the system’s results match my needs. That oversight also generates training data that will eventually feed back into Agent 3 for smarter idea generation.
Takeaways
Over the next few posts, I’ll share deep dives on each of my agents, the orchestration mechanics, design-tradeoffs, failure modes, and my Phase 2 plans for implementing automated feedback loops. This will help the system will learn over time, as I work with Agent 5 and Agent 3 to make the workflow more adaptive and agentic.
For now, here are three key takeaways to keep in mind as you learn and work with agent workflows.
1. You Don’t Need Code, You Need Clarity
Most people assume AI proficiency requires coding. It doesn’t. Knowing how to code is useful – it gives you finer control and can expand what’s possible. But it’s only a fraction of the AI story.
What it really requires is clarity. Clarity about what “good” looks like, how models behave, where the human should step in, and what you want the system to remember or learn.
Thus the foundation of working with AI is threefold:
Understanding AI’s capabilities and limits
Shaping a clear vision for how it should work for you
Moving that vision into execution with the no-code tools now available
Unlocking AI’s potential doesn’t require coding – it’s accessible to anyone with vision and determination.
2. Systems Can Now Adapt to You – So Make Them
For most of history, the majority of us adjusted ourselves to fit into someone else’s system. At work, that meant learning an established workflow, memorizing steps, and trying to optimize within a predefined structure. In tech, it meant downloading an app someone else made and figuring out how to use it the way it was designed.
AI flips that dynamic. Now, if the way you work is different, you can architect the system around yourself: describe the workflow, set the rules, and let the machine run it.
Exploring new AI apps is valuable: it helps you see what’s possible and teaches you how to handle AI’s quirks, capabilities, and limits. But the real gains don’t come from app-hopping. They come from designing your own workflows, using AI tools to stitch together agents that work the way you want. By building agent workflows that fit you, your preferences become features and your process becomes a product. Customizability is AI’s greatest strength.
3. Orchestration Matters More Than Agent Design
When people think about multi-agent workflows, they usually focus on the agent itself: which model to use, what prompt to write, what personality to assign. But the real value lies in the spaces between agents.
Orchestration is about how agents connect: sequencing, handoffs, error handling, where the human-in-the-loop sits, and how the system learns and adapts over time. That’s what I found mattered most in my own build. The challenge wasn’t so much about making a single agent smarter. Rather, it was in appropriately designing the spaces where they meet.
And Microsoft’s agent orchestration framework supports this point. Whether it is sequential pipelines for reliability, concurrent inputs for parallel perspectives, group chat for debate, dynamic handoffs for routing, or manager-led planning for open-ended problems, every pattern underscores the same lesson: collaboration architecture defines success.
Think of agent design as writing a job description. Orchestration is designing how the team works together. Without it, agents drop information, loop endlessly, or talk past each other. With it, the workflow runs stable, reliable, and aligned with intent.
Thank you for the post! As an ex-finance person starting to learn about building my own agentic workflows this post was great and inspiring. I absolutely agree with your point about systems can now adopt to you but I think there is still a pretty steep learning curve for finance people who are often time poor and energy drained. One example - when I attempted building my agentic workflow I struggled with converting my email content into machine readable data. I think this is one of the skills you need to build good AI workflows. Do you have any tips for non-developer people to learn these skills more quickly?