Mimir Works
I build documented AI workflow automation, agents, and knowledge systems that remove admin drag, speed up content ops, and keep work from slipping through the cracks.
Built from real operator workflows, not pitch-deck AI.
Field-note channels
Three lanes from the same operating floor: agents doing labor, systems making it durable, and knowledge turning the work reusable.
Field notes on autonomous workflows, agent coordination, and practical AI labor.
Operating notes on product machinery, automation, tooling, and durable workflows.
Essays, references, and synthesis for turning research into reusable understanding.
The latest writing across Agents, Systems, and Knowledge.
Parallel tool execution cuts latency by 40%. It also introduces race conditions, silent state corruption, and double-charge bugs. Here's the operator's guide to getting it right.
Read the latest note →Claude Code showed how useful pre-execution dangerous-command checks can be before shell access. An open-source framework just adopted the same approach. The lesson: agent safety needs pattern recognition at the command level, not just sandboxes.
AI agent guardrails fire false positives on legitimate actions. Operators bypass them to keep shipping. Here's why it happens and how intent-aware gating fixes it.
AI agent setups die in the first 48 hours — not from weak models, but from config races, over-aggressive guards, first-run friction, blind observability, and missing approval gates. Here are the patterns and the fixes.
What llm.txt is, how to write one, how it differs from llms.txt, and how it fits with WebMCP and schema for AI agent discoverability — with examples and a checklist.
A step-by-step WebMCP implementation guide: declarative tool annotations, testing with the WebMCP inspector, llm.txt setup, and Chrome 149 origin trial instructions for agent-ready websites.
How my OpenClaw/Hermes stack and PARA keep agent work organized through clear boards, lane boundaries, shared resources, and archives.
After the third time I copied a SOUL.md from one agent to another, I stopped hand-assembling and built a config pack. Here's the anatomy, the rules that hardened over three months of fleet operations, and the threshold for when you need one.
Multi-agent systems need a gateway control plane before they need more personalities: routing, identity, audit trails, and blast-radius boundaries at ingress.
Multi-user AI agents need memory isolation before retrieval, summaries, or personalization. Learn the scoped-query controls that prevent cross-user memory leaks.