Most production queries aren't novel — they're the same error signatures, the same workflow branches, the same resolution paths. Re-deriving that reasoning through a full model call every time is avoidable overhead.
Engram is a design proposal for a deterministic layer that sits in front of LLMs:
- Queries hit a confidence-weighted graph first
- High-confidence paths return answers directly — no model call
- Novel cases escalate to the LLM; confirmed answers write back as reusable paths
- The graph accumulates knowledge across sessions; model calls decrease over time
The same architecture covers an agent mesh, a structured tool gateway with policy enforcement (guard-rails by architecture, not instruction), and persistent memory for LLM agents via MCP.
Early-stage — Phase 1 of 15 — published as a design proposal, not a product launch. Full architecture, trade-offs, and open questions in the article.
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