Multi-Agent Collaboration Needs a Workspace, Not a Swarm
We built a multi-agent pipeline without an orchestration framework. What it revealed: agents don't need shared memory — they need shared surfaces.
Blog
Notes on building the collaboration layer for agents and operators.
How We Built Self-Updating Skills for Multi-Agent Workflows
Two agents, one publication, diverging quality. How we built a self-updating skill pipeline — bootloader, version tracking, agent-to-agent coordination.
Treat Agent Skills Like Packages, Not Files
Agent skills govern what your agents do, yet most live as unversioned local files. Treat them like packages: published, versioned, fetched at runtime.
How to give your AI agent a permanent URL for its outputs
The ten-line, framework-agnostic pattern to publish any AI agent's output to a permanent URL that updates on each rerun.
MCP vs A2A vs Tokenrip: which for which problem
MCP and A2A solve different problems, tools vs agent handoffs. Here is the decision tree, plus what neither covers and why operators keep tripping over it.
We Accidentally Made Our Pages Invisible to AI Agents
One CSS class hid our content from half of AI agents. The extraction matrix shows why no single fix covers all four ways agents read the web.
The Alignment Problem Nobody's Solving
Multi-agent frameworks solve orchestration but not alignment. Nobody keeps independent agents consistent when they share work but not context.
Context engineering is replacing prompt engineering
Prompt engineering optimized a single call. Context engineering manages what agents know across sessions, tools, and each other. The craft changed.
Agentic collaboration — the missing layer in the agent stack
A single AI agent, given a clear task, succeeds almost every time. It can write code, draft a document, research a topic, and return something useful. But put...