Vinay Aradhya is Director Product Management at Thomson Reuters.
In this series, “AI As Co-Founder,” Phase One focused on how entrepreneurs and founders can use AI as a thought partner to challenge assumptions, sharpen market understanding and validate problems before they even begin building anything.
Phase Two gave a step-by-step breakdown of how to build a purpose-built agentic system for customer validation, wired together with Model Context Protocol (MCP).
This phase is where things start to compound.
Most founders don’t struggle to build something. In fact, they’re usually really good at it. Where they struggle is getting anyone to care about it. So, they spend their time focused on product development; they’ll worry about getting users later.
But here’s the rub: After you’ve spent months building your product, refining features and creating roadmaps, you get to the point where the cost of changing direction is too high. What follows is a familiar scramble to generate interest.
Product-led growth (PLG) lets you create demand and customers while you develop your product. The question I come back to is simple: What is the smallest experience I can give someone that delivers real value in a few minutes and makes them want to come back?
That early moment, that first signal of value, becomes the starting point for demand.
From Funnel To Flywheel
Traditional marketing still leans heavily on funnel thinking. You bring in a large number of people at the top, move some of them through stages and eventually convert a small percentage at the bottom.
What’s more useful is thinking about marketing in terms of a flywheel. One person finds value and takes an action, and that action naturally leads to another person discovering the product.
The challenge is that running a system like that usually requires a team. You need people doing research, creating content, monitoring behavior and constantly adjusting the approach. Most founders at the beginning do not have the capital or the bandwidth to build that kind of team.
This is where agentic AI can help. Instead of waiting until you can hire a growth team, you can assemble one using agents that take on specific roles.
Designing The Agentic Flywheel
The structure I use is grounded in the same product-led loops most teams already understand. Acquire, activate, retain, refer. The difference is that each of these loops can be supported by an agent. Whereas in Phase Two, the agents focused on research, here they focus on growth.
The Researcher Agent is your starting point. Its role is to build context for the rest of the system. It looks at where the target audience is spending time, how they describe their challenges and what signals suggest they are actively looking for a solution. The output is a living brief that every other agent works from. I’m currently using this exact system for my own customer discovery to explore problems faced by product managers. The Researcher Agent is already surfacing signals that are shaping how I think about the problem space. I’ll share what I find in a future piece
The Creator Agent takes that brief and turns it into something tangible. This could be a simple tool, a short diagnostic, a template or a lightweight report—something that earns attention without asking for it first. This is product-led growth at the top of the funnel, continuously refreshed based on what is resonating with your audience.
The Activator Agent runs the “aha” moment. It focuses on what happens after someone shows up. What did they try? Where did they stop? It identifies users who are close to value but have not quite reached it, and it triggers the right intervention, whether that is a contextual tip, a feature prompt or a short walkthrough.
The QA Agent is the gatekeeper that reviews every message, nudge and piece of content before it reaches the user. Is it accurate? Is it relevant to that specific user’s behavior? Does it feel like it came from a product that understands them? One misaligned message at the wrong moment can break trust quickly.
The Analyst Agent pulls everything together. It tracks what is working across the flywheel. Which inputs drive activation, which nudges convert and which users begin to show referral behavior. That output feeds back into the Researcher Agent, tightening the system over time.
Starting Without Overengineering
One of the biggest misconceptions I see is that this requires a complex setup. It does not.
In the first week, you can stand up something functional with a simple PLG + AI stack that answers three questions: What delivers value? How is it measured? How does the system respond?
Here are the layers I suggest:
Value Delivery: This is a free landing page that works without a login. Consider tools like Webflow or Carrd for the page and Typeform or Tally for input.
Capture And CRM: This is email capture tied to the value itself. This is not a gate before access but a way to continue the experience. Use tools like ConvertKit or Beehiiv for the list, HubSpot free tier for lightweight CRM and Apollo if you are running outbound alongside it.
Behavioral Signal: This is where you define what a product-qualified lead looks like. You need a clear usage threshold that predicts conversion. Without this, the activator has nothing to act on.
Orchestration: This is a connector that passes information between steps. Tools like Make, N8N or Zapier can handle this without code.
What matters is not the tools themselves but how they are connected. When data flows cleanly from one step to the next, even a simple setup starts producing useful insight quickly.
Moving The Needle
There is a natural concern that once AI starts generating content and outreach, everything begins to feel essential. It is also easy to mistake activity for progress. Messages go out, data comes in, dashboards fill up. It can look like things are working.
The principle I come back to: Stay close to behavior. The more the system reflects what the user is already doing, the more useful it becomes.
The signals that matter: How quickly someone reaches their first moment of value. How many people who try the product actually get there. Whether certain behaviors consistently lead to deeper engagement. And whether people come back on their own after the first interaction.
When users return without being prompted, it usually means the product gave them something worth coming back for.
There is also a practical check I use. Is this helping me move faster and make better decisions, or am I spending more time tuning it than actually moving forward? At an early stage, that tradeoff shows up quickly.
If you look across these three phases together, the pattern becomes clearer. It starts with understanding what problem is worth solving. It moves into building a way to explore that problem with structure and discipline. And then it extends into how the product reaches people while it’s still being built, creating momentum before you ever launch.
What’s Next
Running a growth flywheel on agentic AI creates a new category of problem. When agents are generating content, triggering nudges and acting on behavioral data at scale, the question shifts from whether it works to what happens when it does. Volume exposes every assumption you made about quality, safety and user trust.
A system that runs reliably for 100 users starts breaking in ways you didn’t anticipate at 10,000. Content that felt personalized starts feeling generic. Nudges that converted early users start annoying the next cohort. And the governance questions; who owns the output, how do you audit it, what happens when an agent makes a wrong call, become urgent instead of theoretical.
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