Persistent organizational memory for every agent.
Solves the statelessness problem. Every AI agent in LoomStack operates with full organizational context — architecture, code ownership, past decisions, and deployment history.
AI without context produces technically correct, organizationally wrong output.
Every AI coding tool that operates without your organizational context will eventually produce a change that violates an unwritten rule, breaks an ownership boundary, or repeats a past mistake. The Context Layer eliminates that failure mode.
Organizational Memory Graph
A continuously updated knowledge graph of your engineering organization — services, ownership, architecture decisions, incident history, and coding standards. Agents never start from zero.
Real-Time Context Injection
Every agent workflow receives precisely the context it needs, retrieved at execution time. No stale data, no missing dependencies, no blind spots.
Cross-Tool Synthesis
Pulls from GitHub, Jira, Slack, PagerDuty, Confluence, and your observability stack. One unified graph from fragmented tribal knowledge.
Historical Pattern Learning
Past incidents, rollbacks, and review outcomes inform future agent behavior. The system is designed to learn what went wrong before and avoid repeating it.
What the graph knows
How context flows
Ingest
Continuously syncs from your engineering tools — Git history, tickets, docs, incidents, chat.
Structure
Raw data is parsed into a typed knowledge graph with relationships, ownership, and temporal context.
Retrieve
When a workflow starts, relevant context is retrieved based on the services and code paths involved.
Inject
Each agent receives a tailored context window — only what it needs, structured for its specific task.
Context designed to be retrieved and injected at workflow start. Architecture targets sub-50ms context assembly so agents begin work with organizational memory — not ad hoc fetches mid-task.
Give your AI agents the context they've been missing.
Stop watching AI produce technically correct but organizationally misaligned output.