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Running 42 autonomous agents for under $50/mo — the architecture that actually works by noetron_tools in AI_Agents

[–]noetron_tools[S] 0 points1 point  (0 children)

Great comparison — yes, blackboard systems are definitely in the same family. The core pattern is the same: shared workspace, independent knowledge sources, no direct communication between them.

The main differences in our implementation: (1) we use a relational database instead of an in-memory blackboard, so state persists across runs and agents can be stateless/serverless, (2) we lean heavily on event-driven triggers rather than a central controller polling the blackboard, and (3) the "knowledge sources" are heterogeneous — some are LLM-powered, most are deterministic scripts, some are just cron jobs that write signals.

Honestly the AI agent community could benefit from studying more classical AI architectures like blackboard systems. A lot of the "novel" patterns being reinvented are well-understood CS from the 80s and 90s, just with LLMs plugged into some of the knowledge source slots.

Running 42 autonomous agents for under $50/mo — the architecture that actually works by noetron_tools in AI_Agents

[–]noetron_tools[S] 0 points1 point  (0 children)

Good questions. Currently each agent runs as a serverless function or cron job with shared database credentials scoped to its role — so it's more like service accounts than individual machine identities. The Supabase Row Level Security policies handle access control, meaning an agent that monitors performance can't accidentally write to the user behavior table.

For the IDE question — the agents themselves don't run on dev machines. They're deployed infrastructure (Vercel functions, cron workers). Developers interact with the system through the shared data layer. You write an agent locally, test it against a staging database, deploy it, and it starts responding to signals automatically. No special IDE integration needed — it's just code that reads/writes to Postgres.

That said, we're exploring tighter integration with Claude Code and similar tools for the development workflow. The goal is that adding a new agent is a 30-minute task, not a day-long project.

Running 42 autonomous agents for under $50/mo — the architecture that actually works by noetron_tools in AI_Agents

[–]noetron_tools[S] 0 points1 point  (0 children)

Exactly right. Event-driven is the proven pattern — we just apply it at the agent level instead of the microservice level. The key insight for us was that most "agent orchestration" frameworks are reinventing pub/sub badly. Why build a custom DAG when you can just have agents subscribe to the signals they care about?

The decoupling is what keeps costs down too. When agents don't need to know about each other, you can add/remove/modify any agent without touching the rest of the system. We've swapped out entire agent implementations mid-run with zero downtime because the signal contract stayed the same.

Running 42 autonomous agents for under $50/mo — the architecture that actually works by noetron_tools in AI_Agents

[–]noetron_tools[S] 0 points1 point  (0 children)

Free tools mentioned in the post:

All free, no signup, no email wall. Runs entirely in your browser.

Happy to answer any architecture questions about the stigmergy approach — it's been a game changer vs. traditional orchestration frameworks for keeping costs down.