Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked

Building AI agents has become significantly easier due to mature infrastructure and frameworks, yet these systems frequently fail because they lack essential organizational context. While developers can now deploy agents with minimal effort, these systems often operate with a narrow understanding of internal processes, leading to "confidently wrong" outcomes. Unlike humans who rely on tribal knowledge—such as Slack discussions, postmortems, and documentation—standard agents lack this synthesized intelligence. A context engine solves this by reconciling scattered data across an organization, providing agents with grounded, relevant information rather than raw, unorganized data. By shifting from simple data access to true organizational understanding, this approach prevents silent failures and enables agents to act with the same institutional awareness as an experienced team member, effectively bridging the gap between raw model intelligence and reliable, context-aware execution.