Building the Context Engine AI Agents Need
summary
Claim: agents fail from missing organizational context, not intelligence. A context engine built by Unblocked ingests Git, Jira/Linear, Notion/Confluence, Slack/Teams, Sentry/Datadog, runs embeddings and entity-linked graphs, enforces permissions, resolves conflicting sources by recency/authority/proximity, and returns identity-scoped, token-optimized payloads for humans or agents. Tests show doom-loop reduction from 2.5 hours → 25 minutes and 50% fewer tokens by one-shot prompting; open-source pieces include a social graph and repo-rules agent plus a context-engine simulator.
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chapters (14)
- 0:00Introduction
- 2:11AI agents lacking context
- 3:13Human to automated context
- 4:59Static context and MCPs limits
- 7:02Myths about context engines
- 9:08Requirements for context engine
- 11:12Processing and delivering context
- 13:40Time and token efficiency gains
- 15:03Open-source context engine tools
- 18:09Beyond code generation
- 19:33Team’s context maturity
- 20:35Future of AI agents
- 26:28Q&A: Unblocked vs enterprise search
- 28:15Q&A: Conflicting context handling
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