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ContextChain: Structured Agent Handoffs
The Problem Multi-agent systems hand off context via raw dumps (expensive, noisy) or compressed summaries (lossy — they drop the why, not just the what). As pipelines grow, by the 3rd or 4th handoff, agents reason over degraded context, causing inconsistent outputs. The Solution ContextChain introduces the Episodic Memory Object (EMO) — a structured, typed primitive built for agent-to-agent handoff. Each agent emits an EMO capturing decisions, reasoning trace, and provenance from its step, so downstream agents inherit exactly what they need without re-deriving context or losing fidelity. How It Works We demo ContextChain on a 4-agent enterprise procurement pipeline. Each agent passes an EMO to the next instead of a context dump. A RAG layer over a vendor knowledge base supplies grounded context (pricing, vendor history, terms) at each step. Outputs stream live via FastAPI + SSE, making the full reasoning chain observable as it happens. Tech Stack LLM inference via AIML API and Featherless models; FastAPI + SSE streaming backend; RAG layer over a vendor knowledge base; EMO schema as the core handoff structure. Status & Roadmap Core pipeline and EMO schema work end-to-end on the demo. Remaining: Band SDK integration and scaling the vendor knowledge base. We plan to publish EMO as a general primitive for multi-agent handoff via an arXiv paper.
19 Jun 2026
