Stop babysitting your agents... — Brandon Waselnuk, Unblocked
May 26, 2026 · 18:54
Brandon Waselnuk of Unblocked argues that the bottleneck for AI coding agents is not access (MCPs, tools) but understanding—they need a context engine that builds a research packet from codebase, Slack, PRs, and org structure before generating code. He debunks three myths: naive RAG suffers from satisfaction of search, more MCPs don't provide reasoning, and million-token windows don't enable effective reasoning. His team's context engine reduced a Zendesk integration task from 2.5 hours and 20.9M tokens (with MCPs only, but code that would have broken production) to 25 minutes and 10.8M tokens, earning a senior engineer's approval with one nitpick. Three hard lessons: optimize for understanding not access, resolve conflicts rather than hide them (e.g., Slack thread where CTO says code is wrong), and never cache answers because context changes daily. He demonstrates a social graph tool (open-sourced Monday) that maps engineers to code areas and collaborators, and shows a demo where the agent's MCP calls the context engine to generate a plan covering factory patterns, library modules, and client registration.
