I killed my "commodity SaaS" to build an AI Growth Team. Here’s why.
For the last few months, I was building another lead-gen tool. It was functional, fast, and... completely boring. I realized I was building a "commodity"—something people might try once but would never truly rely on because it just added more work to their plate.
I had a "burnout moment" last week where I almost deleted the repo.
But then I looked at what actually works for growth. It’s never just "getting a list of emails". It’s the coordination between research, content strategy, and personalized outreach.
The Pivot:
Instead of building a tool, I’m building a coordinated team of AI agents.
Each agent has a specific "job description":
• content_advisor: Frames the narrative based on the specific lead's pain points.
• outreach_specialist: Manages the actual relationship.
and others agents
They talk to each other. They share context. They work like a $10k/mo marketing agency, but they cost almost nothing to run.
I'm calling it Multify. We just pivoted the entire architecture to focus on this "Agent Team" approach rather than standalone features.
I’d love to hear from you:
1. Have you felt the "SaaS commodity" fatigue lately?
2. Do you think autonomous agent teams are the future of growth, or are we just adding more noise to the internet?
You can check out the new direction here: Multify
I'm happy to answer any questions about the tech stack or the pivot!
I really respect this pivot. A lot of SaaS products end up feeling like temporary tools instead of real long-term solutions. Shifting from a single feature to a coordinated AI team sounds more aligned with how growth actually works in practice. It’ll be interesting to see how this approach performs over time.
Excellent! in the medium and long term it tends to improve! more people entering the world of AI and adapting to new ways of interacting with software
Super interesting pivot! Creating a team of AI agents seems to be a game-changer for scale. I'm also using agents in support workflows — I've put in place strict rules (max 1 reply per ticket + auto-escalate) to avoid hallucinations. Escalations have reduced significantly in early pilots. How are you handling hallucinations in your agent team?
Killing a working product to rebuild it as something with a wider surface area is a hard call — especially when the first one has paying customers. The "commodity SaaS" framing is interesting though. Was it more that the TAM felt capped, or that you couldn't differentiate on features alone against better-funded competitors?
I managed to unlock my potential and now I have more clients! If it's difficult, then it's because it's the right thing to do! Nothing is easy, you have to go after things and not just stand still waiting for everything to be handed to you! Things don't move forward if you don't wake up, that's the reality.
Congrats on the pivot! I find the 'agent team' approach much more compelling than another commodity tool. How do you ensure 'contextual integrity' between different agents? When the content_advisor and outreach_speacialist share data, how do you prevent hallucinations or loss of nuance as the lead moves through the pipeline?
This is an interesting approach to the early distribution problem many indie founders face.
What stood out to me is the focus on detecting real user intent instead of just generating leads through keywords or generic outreach. Tools like Multify try to surface conversations where people already express a specific problem, then filter them through AI to match an Ideal Customer Profile before sending alerts.
That kind of signal-based lead discovery can be powerful because it shifts the effort from cold prospecting to responding to existing demand.
It’s a good reminder that for many startups, distribution and timing matter just as much as the product itself.
Yes! What makes a startup successful is distribution! this is a crucial pillar
The “SaaS commodity” fatigue is real. Standalone tools that output data without strategic orchestration inevitably become replaceable. The defensibility now is in workflow integration + context continuity, not features.
The agent-team direction makes sense — especially if the agents share memory, refine ICP assumptions over time, and optimize toward revenue signals (not vanity metrics). Otherwise, it risks becoming automation theater.
My question:
How are you ensuring these agents improve performance through feedback loops — are they learning from closed deals and real conversion data, or just executing predefined logic?
I think it is limited.
This pivot is brilliant, John! 🚀
The shift from "Standalone Tools" to "Agentic Teams" is exactly where the industry is heading. We’re moving from providing users with a "hammer" to providing them with a "carpenter." Your point about "Productive Ambiguity" and context-sharing between agents is the secret sauce most people are missing.
I’m navigating a similar journey in the gaming niche. After years of seeing "commodity" scripts that were too predictable, I’m building AI Powered Game Dev for Beginners. My goal is to teach devs how to build exactly what you’re describing: Coordinated Agent Teams. Instead of hard-coding an NPC to "walk to point A," we’re training them to act as "Tactical Specialists" that share context with the game world. It’s the same philosophy as Multify, just applied to 3D environments.
Question for your tech stack: Are you using an orchestration framework like LangGraph or CrewAI for the agent communication, or did you build a custom state-sharing layer?
Looking forward to seeing Multify grow! For those interested in the technical roadmap of how we're architecting these Game AI agents, feel free to visit my profile and check out the link in my "Product" section. 🎮🧠
thats actually really cool and interesting. im wondering how you started it?
With a pain of its own
Re: your questions: 1. Absolutely on SaaS commodity fatigue. The bar for "useful" has gotten so much higher. 2. Autonomous agent teams are promising, but the operational complexity is real. One thing I'd consider early: as you scale those agent interactions, LLM costs can get wild pretty quickly (especially with the back-and-forth between agents). I've seen teams go from "almost nothing" to thousands/month overnight when they hit their first growth spike. Might be worth building in some cost monitoring from the start - even simple tracking of token usage per agent type. We learned this the hard way with a similar multi-agent system where the content_advisor was burning through way more tokens than expected due to context switching. What's your current thinking on managing the agent-to-agent communication costs as you scale? If helpful, here 's the link zenllm io
We have scalable infrastructure and audit everything! our models have native caching, we also manage contexts and tokens with RAG so we are fully prepared and the agents have great orchestration!
Running something similar for my own outreach .The hardest part turned out to be the research layer, specifically how you decide when a signal is warm enough to act on vs. just noise. How are your agents deciding that threshold?
Respect for killing the “commodity” version — that’s a hard call. The “agent team” idea makes sense, but the moat is going to be accountability + measurable outcomes, not the agent roles themselves.
If you can make it reliably produce something like “X qualified conversations booked / week” (or “Y validated ICP insights”), that’s real value.
Two questions:
What’s the single north-star metric you’ll optimize and charge for (calls booked, pipeline, retention, insights)?
What guardrails prevent the outreach agent from turning into spam at scale (rate limits, human-in-the-loop, personalization constraints)?
The commodity trap is real and I think lot of builders fall into it without realising.
We see same thing on client side also. Founders come to us wanting to build "another food delivery app" or "another fitness tracker." Functional yes. But nothing that makes user choose them over 10 existing options.
AI agents coordination angle is interesting pivot honestly. The value is not in one tool doing one thing. Its in multiple things working together without human managing each step.
Curious how you handling the "explain this to non technical founder" problem. Because AI agents sound complex to most people who just want leads 😄
Haha, very good! I would explain it this way: You can pick fruit by yourself, but with a machine you can pick much faster and with much more efficiency!
If the person is smart, they'll understand what I'm talking about!