Engram: A Deterministic Layer for LLM-Powered Workflows
Engram is a design proposal for a deterministic layer that sits in front of large language models (LLMs) to handle repetitive queries and improve efficiency.
Why it matters
Engram's deterministic layer could improve the efficiency and cost-effectiveness of LLM-powered workflows by reducing unnecessary model calls.
Key Points
- 1Queries hit a confidence-weighted graph first before escalating to the LLM
- 2High-confidence paths return answers directly without a model call
- 3Novel cases escalate to the LLM, and confirmed answers are written back as reusable paths
- 4The graph accumulates knowledge across sessions, reducing model calls over time
Details
Engram aims to address the overhead of re-deriving reasoning through full model calls for repetitive production queries. It proposes a deterministic layer that sits in front of LLMs, where high-confidence paths return answers directly without a model call. Novel cases escalate to the LLM, and confirmed answers are written back as reusable paths. Over time, the graph accumulates knowledge, reducing the need for model calls. This architecture also covers an agent mesh, a structured tool gateway with policy enforcement, and persistent memory for LLM agents.
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