Brandon Waselnuk
DevRel at Unblocked, a Vancouver-based developer tools company building context-engine infrastructure for AI-driven software development. Waselnuk is a YC founder and public speaker/author on context engineering, MCP, and helping AI coding agents produce mergeable code.
Agent Autonomy Makes Organizational Context an Engineering Requirement
Brandon Waselnuk of Unblocked argues that the main constraint on AI coding agents is not model intelligence but missing organizational context—the accumulated knowledge of conventions, decisions and incidents that human teammates acquire over time. In Unblocked’s same-prompt test, adding a context engine cut token use from 20.9 million to 10.8 million and reduced runtime from two hours and 33 minutes to 25 minutes. He contends that curated documentation and MCP access alone do not solve the problem, because agents need to weigh conflicting, current and permissioned information rather than merely retrieve it.
Context Engines Make Coding Agents Mergeable, Not Just Functional
Brandon Waselnuk of Unblocked argues that coding agents are failing less because they lack access to tools than because they lack organizational context. In his account, MCP connections, larger context windows and naive RAG give agents more material, but not the judgment to know which code patterns, Slack decisions, ownership signals or backwards-compatibility rules matter. His proposed answer is a runtime context engine that reasons across code, PRs, documents, conversations and social structure before the agent writes code, so its output is closer to something a long-tenured engineer could merge.