Ask HN: What is the "Control Plane" for local AI agents?

It's not for your use case per se, but at Tacnode we're building what we call a "context lake" that's essentially a living, queryable context layer. It's got way more horsepower than you're describing. Built instead for enterprise-scale needs.

So basically agents always have the most up-to-the-millisecond data freshness they need to make good decisions.

I solve this exact problem running OpenClaw (24/7 agent orchestrator). Here's what actually works after months of iteration:

The core insight: Multiple agents with zero shared state = chaos. Solution: State files are your control plane.

Architecture: 1. HEARTBEAT.md — Every agent reads this before acting. Routes to current priorities, cascades work through a defined sequence (not random agent picking a task) 2. TODO.md — Single source of truth with sections: "DO NOW" (unblocked), "BLOCKED" (waiting), "DONE" (completed). Agents read this, not chat history or logs 3. active-tasks.md — Breadcrumbs from interrupted runs. If an agent crashes, the next session reads what was interrupted and resumes 4. Sub-agent output files (qa-status.md, conversion-tracker.md, etc) — Each specialized agent writes structured results. Prevents re-running the same analysis.

The key: State files are deterministic. No "what was I thinking?" after a crash. The next agent reads the same input, same structure, same decision rules.

Terminal log management: - Each agent logs its own session ID (e.g., [agent-001-mar05-0430]) - Output files are prefixed by agent type (qa-, scout-, coo-) - Logs are sent to daily files (memory/2026-03-05.md) for searchability

Deployment: I run ~8 agents on cron (QA watchdog, scout, content factory, conversion tracker, etc). They wake up, read their input files, write results, die. No persistent terminal. No state collision.

The problem you're hitting is probably: agents don't know what other agents already did. Solution is just: everything runs through a shared TODO.md and writes results to named files.

Happy to share templates if useful. Full patterns at: https://osolobo.com/first-ai-agent-guide/

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