Ask HN: Has anyone built an autonomous AI operator for their side projects?
I spent the last month building what I call an AI operator - an autonomous agent that manages my side projects end-to-end while I focus on strategy. It runs on a 30-minute heartbeat loop, publishes daily blog posts, monitors Stripe for sales, checks sites are up, and does directory submissions. It knows when to escalate to me (financial decisions, strategic pivots) and when to just handle things. The hardest part was writing the decision tree - not the AI itself but defining what it owns vs. what needs human judgment. Current setup: main agent handles orchestration, a builder sub-agent handles code/deploys, an amplifier handles content/social. Revenue is small so far ($200 from PDFs) but the system works while I sleep. Curious if others have gone down this path and what broke for you.
4 comments
Yes — I've been running this for my solo SaaS company since January. Not a side project, it's the actual business operations layer.
Similar architecture to yours but I went with specialized agents instead of a single orchestrator + sub-agent split. I have a COO agent that coordinates everything, plus dedicated agents for marketing (writes and posts tweets autonomously via scheduler), accounting (IVA/tax compliance — I'm in Portugal so this is non-trivial), CFO for pricing, CTO for architecture decisions. They share a persistent knowledge graph so context carries across sessions.
The decision tree problem @rodchalski mentions is real. My approach: agents are autonomous by default for their domain (marketing posts content without asking me), but anything cross-domain goes through the COO for coordination. The founder only handles: manual portal logins, irreversible legal filings, strategic pivots, and actual coding. Everything else just runs.
What broke: memory corruption from concurrent writes to the knowledge graph (fixed with mutex + atomic writes). Agents hallucinating product URLs that don't exist (fixed with a domain registry they must check). Content that sounded like ChatGPT wrote it, not a developer (fixed with explicit voice guidelines and a quality gate).
The hardest part isn't the AI — it's defining the boundaries clearly enough that agents don't drift. Wrote about the full setup here: https://dev.to/setas/i-run-a-solo-company-with-ai-agent-depa...
Similar architecture to yours but I went with specialized agents instead of a single orchestrator + sub-agent split. I have a COO agent that coordinates everything, plus dedicated agents for marketing (writes and posts tweets autonomously via scheduler), accounting (IVA/tax compliance — I'm in Portugal so this is non-trivial), CFO for pricing, CTO for architecture decisions. They share a persistent knowledge graph so context carries across sessions.
The decision tree problem @rodchalski mentions is real. My approach: agents are autonomous by default for their domain (marketing posts content without asking me), but anything cross-domain goes through the COO for coordination. The founder only handles: manual portal logins, irreversible legal filings, strategic pivots, and actual coding. Everything else just runs.
What broke: memory corruption from concurrent writes to the knowledge graph (fixed with mutex + atomic writes). Agents hallucinating product URLs that don't exist (fixed with a domain registry they must check). Content that sounded like ChatGPT wrote it, not a developer (fixed with explicit voice guidelines and a quality gate).
The hardest part isn't the AI — it's defining the boundaries clearly enough that agents don't drift. Wrote about the full setup here: https://dev.to/setas/i-run-a-solo-company-with-ai-agent-depa...
depends on cost of running it and determining if ROI is there...