Context Engineering for Multi-Agent Systems

Example Use Cases

• 1

  Transparent and observable reasoning with interactive trace dashboards.

• 2

  Domain-agnostic orchestration across multiple use cases without code changes.

• 3

  High-fidelity retrieval augmented generation with verifiable citations and safeguards.

Description

This repository provides a production-ready blueprint for creating a universal Context Engine that orchestrates multi-agent systems (MAS) with high-level semantic orchestration and the Model Context Protocol (MCP). It enables building dynamic, transparent, and observable AI workflows that replace rigid hard-coded processes, supporting cross-domain use cases such as legal compliance and strategic marketing. The architecture includes features like dual high-fidelity RAG pipelines, token and cost analytics, and safeguards against prompt injection and data poisoning, making it scalable and reliable for production deployment.