TIL

I built a 3-agent dev team (Bolt writes code, Sage reviews, Nova researches) and immediately hit the same problems any engineering manager faces: who owns what, how many tasks can each handle, and how to route work.

Per-agent concurrency limits (max 1 task each, 3 total), per-agent workspaces (isolated repos), label-based routing, queues when busy. Conway’s Law applies to AI teams too — the architecture of your agent system will mirror the communication structure you design for it.

The review loop was the clearest example: Bolt opens a PR, Sage reviews it, Bolt fixes the feedback, repeat. Capped at 3 iterations — because without a kill switch, two agents will argue forever. Same reason human code reviews have norms against infinite back-and-forth.

The takeaway: don’t think of multi-agent systems as “parallel workers.” Think of them as a team you’re managing. Roles, boundaries, escalation paths, and termination conditions.

Agent skills load context in three levels: metadata (~100 tokens) at startup, full instructions (under 5,000 tokens) when triggered, and resources only as needed.

This is why skills beat .cursorrules - it loads entire contents into every conversation. A skill with 60+ product references only loads what’s relevant. Cloudflare-skill has 60 reference files but you can ask about Workers without paying for WAF documentation.

Every public commit, tweet, or project is a lottery ticket. The more you ship visibly, the more opportunities find you.

tryelements.dev caught Clerk’s attention and got me the job. X grew from 0 to 2500 followers. Ship something every week. Tweet 2-3x per week minimum.

Effective agent design boils down to context management. Context is a finite resource with diminishing returns - like human working memory.

A higher capacity model with caching can be cheaper than a lower cost model without it. Progressive disclosure: don’t load all tool definitions upfront. Successful agents use surprisingly few tools (~12 for Claude Code, <20 for Manus).

Autonomous AI coding loop that ships features while you sleep. Named after The Simpsons character - embodies persistent iteration despite setbacks.

The core insight: context pollution is inevitable. Ralph doesn’t try to clean memory - it throws it away and starts fresh. Progress persists via files + git. Failures evaporate.

while :; do cat prompt.md | agent ; done

Each iteration reads task list, picks highest priority, implements, runs tests, commits if passing, logs learnings, repeats.

Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Think of it as “USB-C for AI applications” - a universal connector.

Before MCP, every agent needed custom integrations for every service (N×M problem). MCP creates a standardized interface so one MCP server for Postgres works with any MCP-compatible agent. Agents discover tools dynamically via protocol.

In a world where everyone has access to the same AI intelligence, companies differentiate through context - the proprietary information, tribal knowledge, and organizational data that agents receive.

Knowledge workers are becoming managers of AI agents. Agents are only as good as the organizational context they receive. As Peter Drucker said: “if HP knew what HP knows, we would be three times more productive.”

Ship the fast version first. Tight, artificial deadlines spike productivity and reveal that most things can be done faster than scarcity mentality allows.

“Do the five day version” - the constraint forces clarity. Unrealistic deadlines are a useful tool, not a stress source. Ship fast, iterate faster. Nobody cares about your v1 polish.

AI is plummeting the cost of personalization. The web will become more whimsical, expressive, and unique in 2026.

The cost of turning “wildest dreams into pixels” is now accessible. Geocities-era creativity is returning but with AI capabilities. Non-technical people can move away from templates. The template era is dying.

Willpower fluctuates with stress, fatigue, and hunger. Instead of relying on it, build automated if-then rules that remove decision-making.

Nearly half of daily decisions are already autonomous (habits). Create explicit rules: “if 7am, work out” - no decision needed. Every choice taxes mental energy. High performers don’t have more willpower - they remove choice entirely.