Autonomous AI Agent Fleets
Agent fleets in hardened Docker with per-agent budgets—assumes agents will be compromised.

Sealed-bid auction for agent task routing is genuinely novel—nobody else does economic coordination.
Teams building production AI agent systems with coordination and budget concerns
CrewAI · AutoGen · LangGraph
I built Zero Human Labs after spending too much time babysitting AI agent pipelines — manually routing tasks, watching API costs spiral, and debugging coordination failures. With Zero Human Labs, you define your entire agent team (devs, designers, ops, etc.) in one YAML file. Tasks are routed automatically via a sealed-bid auction — each agent bids based on specialization and track record, and the best one wins the task. No manual assignment, no coordination overhead. Key things that make it different: Per-agent wallets + org-level budget caps → no runaway API spend Circuit breakers → agents that misbehave get frozen automatically Smart model routing → saves 30–80% on AI API costs by matching request complexity to the right model Governance presets calibrated from 146 SWARM simulation runs across 43 agent types Free demo at zero-human-labs.com — 1 agent, 1 example workflow, open-source models. Would love feedback from the HN crowd on the auction routing mechanism especially.
Agent fleets in hardened Docker with per-agent budgets—assumes agents will be compromised.
HTTP 402 status codes gate infrastructure access for autonomous AI agents.
One-click VPS deploy for OpenClaw agents with markdown-defined personas.
Another AI agent platform when LangGraph, CrewAI, and n8n already exist.
Managed multi-agent workspace, but ChatGPT, Claude Projects, and Anthropic's built-in task delegation already solve this.
Kubernetes CRDs for AI agent governance when LangSmith already exists.