When the Question Has a Right Answer: ENGRAM Allows Agents to Work from a Brief

Associative retrieval does not fail when a question has a correct answer. It returns the plausible neighbour, states it confidently, and gives you no signal that it did. So ENGRAM now lets a project declare which documents are authoritative – and every agent working in that project inherits that declaration without being told it exists.

No Message Passed Between Them: ENGRAM Agents Coordinate Through Memory

The hard part of running a fleet was never spawning the agents or routing the tasks — your harness already does that. The hard part is what they know, and whether one agent’s finding can reach another without leaking to everyone. That is not a scheduling problem. It is a memory problem.

Where Memories Come From: ENGRAM Learns to Track Provenance and Group by Project

ENGRAM could already remember an enormous amount — articles, code sessions, conversations from half a dozen assistants. What it could … More

ENGRAM Part 1: GraphRAG with Hippocampal-Like Associative Retrieval

Part 1 of this blog series explains how query-entity relevance spreading across a knowledge graph eliminates the need of typical … More

MCP, A2A, and Human-in-the-Loop: A Multi-Agent Threat Intelligence System in Practice

This blog describes a cybersecurity threat intelligence platform built in Python that models relationships between threat actors, attack techniques, vulnerabilities, … More

GraphRAG Part 2 – Cross-Doc & Sub-graph Extraction, Multi-Vector Entity Representation

Part 2: A deep dive into implementation of cross-documents semantic relations extraction, multi-hop graph traversal queries, and multi-vector (entities, relations, … More

Building a GraphRAG System – Core Infrastructure & Document Ingestion

Part 1: A deep dive into multi-database architecture, AI-powered entity extraction, and intelligent document processing Introduction Traditional RAG systems rely … More