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EPIC: Transform DataCortex into Context Engine for AI Agents #10

@tfius

Description

@tfius

Overview

Transform DataCortex from a knowledge graph visualization tool into a full-fledged Operational and Decision Context Layer for AI agents, implementing the "Context Graph" thesis.

Vision

Enable AI agents to access two layers of context:

  1. Operational Context: The "who, what, and when" (Identity, Relationships, Temporal State)
  2. Decision Context: The "why" (Decision Traces, Precedents, Auditability)

Current State → Target State

Capability Current Target
Data Sources Markdown files (Datacore spaces) Multimodal (Telegram, Email, CRM, Code, Logs)
Entity Model File-based nodes, Wiki-links Canonical Entities (Schema.org), Identity Resolution
Temporal State "Pulses" (Snapshots) Continuous Event Stream & Temporal Querying
Decision Tracking Implicit in notes/logs Explicit "Decision Trace" Schema & API
Agent Interface CLI, Claude Code Integration Model Context Protocol (MCP) Server

Implementation Phases

Phase 2: Connectivity (Priority: HIGH)

Phase 3: Decision Layer (Priority: MEDIUM)

Phase 4: Multimodal Integration (Priority: MEDIUM)


Dependencies Graph

#11 MCP Server ──┬──> #14 Decision Schema ──> #16 Precedent Search
                 └──> #16 Precedent Search

#12 External Links ──┬──> #17 Connectors Architecture ──┬──> #18 GitHub Connector
                     └──> #20 Virtual Nodes             ├──> #19 Telegram Connector
                                                        └──> #20 Virtual Nodes

#13 Identity Service ──┬──> #15 Decision Agent
                       ├──> #18 GitHub Connector
                       └──> #19 Telegram Connector

Related Documentation

See expanding_datacortex.md and EPIC_CONTEXT_ENGINE.md for detailed implementation plans with full code examples.

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