Battle-test, challenge, translate, verify: an emergent SOP from five workstreams
This round of five workstreams revealed a reusable SOP. Not pre-designed — grown from practice.
From the Lab
Research & Writing
Deep dives into autonomous systems, agent architecture, and the infrastructure that makes AI work in production.
agents-stack v2.0 — spiral routing, method layer, and a 16→7 phase reduction
agents-stack v2.0 ships a three-layer spiral architecture replacing the linear 16-phase pipeline. Two new capabilities: the Method Layer and the spiral turn.
agents-stack v2 replaces 16 pipeline phases with a 7-step spiral
We cut 8 worker phases, 6 Python scripts, and 3,138 lines of infrastructure by asking one question: does this step actually need an LLM agent?
We built an agent framework 6 times before keeping the first version
Designing from scratch, iterating through 6 versions, and returning to a single while loop. What building an internal agent framework actually taught us.
We Let Two AIs Build the Same AgentOS. Then We Cracked Open the Code.
Same problem, two AI teams. GPT-5.4 built one version. GLM5.1+K2.6 built another. We ran a 10-dimension code review and found the gap isn't at the syntax layer — it's at the systems layer. A reverse-engineering of how two different AIs actually think.
AutoHarness turns prompt engineering into harness engineering
AutoHarness shows that agent reliability belongs in code-level guardrails, not just sharper prompts.
agents-docs-kits keeps agent context small on purpose
A minimal docs kit for teams that want agent context injection, progressive disclosure, and cleaner hand-offs without turning one worktree into a memory dump.
Router skills should route, not secretly do the work
The `create-router-skill` package in agents-docs-kits matters because skill families stop being discoverable once routing logic is hidden in folder names, vague umbrella docs, or silent fallbacks.