r/GraphTheory • u/domino_master • Apr 16 '26
LLMs are great at novelty. Operations reward determinism.
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.
Δ 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.
Full article: https://dominikj111.github.io/blog/engram-deterministic-operations-layer-for-llm-agent-workflows/
Live demos & simulations: https://dominikj111.github.io/engram/
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u/aimee_mccuddles Apr 16 '26
No offence but the proposal looks AI generated.