Inside Unblocked’s Context Engine

A context layer connects AI agents to an organization's knowledge: code, conversations, issues, documentation, product, and production systems. Unblocked is the context layer for agentic software development. It connects and reasons over that scattered context so agents can complete work correctly with less human intervention. A search index tells an agent where information lives. Unblocked explains what it means, and the Context Engine is the part of Unblocked that does that reasoning.

Both terms apply at different levels. Unblocked is the context layer for agentic software development: the infrastructure that connects agents to your organization's knowledge. The Unblocked Context Engine is the reasoning system inside that layer. It decides which sources to trust, resolves conflicts between them, and assembles what the agent receives. For the longer argument, read a context layer is not a context engine.

Retrieval-augmented generation returns the passages that best match a query and leaves the model to sort them out. A context engine reasons before it delivers: it queries semantic vectors, a knowledge graph, and structured data together, resolves conflicts between sources using recency, authority, and proximity signals, enforces permissions, and compresses the result to what the task needs.

Separate MCP servers let an agent retrieve from one system at a time, and each one adds overhead to the context window. The Context Engine answers across all your sources in a single pass through one MCP connection, reconciles contradictions server-side, and returns one ranked answer with citations.

No. Rules files and skills work until they don't. They encode what you already know to tell the agent, and they are only as good as the last time a human touched them. They can't capture the PR that changed a convention, the decision that was never documented, or the Slack thread that overrides a doc. The Context Engine synthesizes continuously from live sources, including code, PRs, conversations, and docs, so your agent works from what's actually true right now. It also turns recurring PR review feedback into per-repository rules that arrive in the agent's context alongside the code, so generated code fits how the team works, not just what the docs say. See the results on mergeable code.

Wiring an agent into your tools takes minutes: there are MCP servers for GitHub, Slack, Jira, and most other systems. What that gives the agent is access. It still has to search each silo, reconcile results that contradict each other, and work out which source is authoritative. All of that happens inside the model's inference loop, so you pay for it in tokens on every task. See how the right context lowers token cost. Some knowledge, such as unwritten conventions and who actually knows a service, exists only as patterns across systems over time, and no single query returns it. The Unblocked Context Engine does the synthesis, conflict resolution, permission enforcement, and ranking server-side and continuously. To measure the difference on your own codebase, run the open-source context-engine-simulator.

Code hosts, issue trackers, documentation, messaging platforms, and product and production data, including GitHub, GitLab, Jira, Linear, Confluence, Notion, Slack, Microsoft Teams, Sentry, and Datadog. See the full list of integrations. Permissions are inherited from each source and enforced at query time.