What Running 34 AI Agents in Production Actually Looks Like

<p>The number sounds impressive until you have to explain what it means. Nova — my personal AI multi-agent system — now runs 34 agents in production. People hear that and picture a dashboard full of blinking lights, autonomous processes spinning up and down, something from a sci-fi pitch deck. The reality is more interesting and more grounded than that.</p><p>Nova started as a way to give myself a proper AI layer across my work: something that knew my projects, remembered context across conversations, and could actually act on things rather than just generate text. It's grown from a single general-purpose assistant into a structured system with agents covering general chat, pipeline execution, ERP-specific tasks, and language processing. Each agent has a defined scope. None of them are doing magic — they're doing specific, bounded things well.</p><p>The production orchestration phase I'm in now is less about adding more agents and more about making the whole system coherent. When you have 34 moving parts, the coordination layer matters more than any individual agent. What routes a request to the right agent? What happens when a task spans multiple agents? How do you avoid the situation where agent A does something that conflicts with what agent B is about to do? These are software engineering problems, not AI problems. The AI is almost the easy part.</p><p>One thing I've learned building this: the value of an agent system isn't in the agents themselves — it's in the memory and context infrastructure underneath them. An agent without persistent, queryable memory is just a stateless API call with extra steps. The reason Nova can be genuinely useful across my projects — AIREP, Find a Sign, Sweeper Parts, client work — is that it has a structured understanding of what those projects are, what's in progress, and what I've already tried. Without that, you're starting from zero every conversation.</p><p>The goal I'm working toward now is a self-improvement loop: agents that can autonomously review Nova's own code, identify weak spots, and propose or make improvements. That sounds ambitious, but the foundation for it is mostly already there. The agents can read code, reason about it, and generate structured output. What I'm building is the scaffolding that turns that capability into a reliable process — one with guardrails, review steps, and enough logging that I can trust what it's doing without babysitting every run.</p><p>What I've resisted throughout this build is treating agent count as a metric. It's easy to add agents. It's harder to make them useful, make them composable, and make the system legible enough that you can debug it when something goes wrong. A 34-agent system that's opaque and brittle is worse than a 5-agent system that's well-understood and reliable. I'm trying to build the latter, and the agent count just reflects the actual surface area of the work I need covered.</p><p>The broader point — one I think about across all my projects — is that AI stops being a tool and becomes a compound advantage when it's integrated deeply enough that it changes how you work, not just how fast you work. Nova isn't a chatbot I query occasionally. It's the backend through which I manage context, surface decisions, and increasingly, execute work. Getting there required treating the AI layer with the same engineering discipline I'd apply to any other production system: clear interfaces, persistent state, failure handling, and an honest assessment of what it can and can't do reliably.</p><p>There's still a lot to build. The orchestration layer needs to get smarter. The self-improvement loop is still more blueprint than reality. But the foundation is solid, and for the first time, the system feels like it's working with me rather than alongside me. That's the threshold worth crossing.</p>