Most production queries aren't novel - they're recurring patterns that have already been solved.
Re-running them through a full model call every time is unnecessary overhead.
Engram is a proposal for a deterministic operations 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, and confirmed answers write back as reusable paths
- The graph accumulates knowledge across sessions; model calls decrease over time
The same architecture works as an agent mesh, a structured tool gateway with policy enforcement, and persistent memory for LLM agents via MCP.
This is early-stage (Phase 1 of 15), published as a design proposal, not a product launch. I wrote up the full architecture - the reasoning, the trade-offs, and what's still an open question.
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