MCP Dev Summit Mumbai 2026

**Space Limited - First Come, First Served.  Please bring a fully charged laptop to the workshop**

As agentic systems evolve from single-step tasks to orchestrated workflows, maintaining structured, evolving context becomes a core engineering challenge. While Retrieval-Augmented Generation (RAG) improves access to information, most implementations treat the knowledge source as a static, read-only repository, making it difficult to capture relationships, causality, and decision history across sessions.

This hands-on workshop focuses on operationalising a context engine using MCP—showing how to build a system where agents can read, write, and reason over shared structured context.

Participants will work through a guided implementation to:

* Design a minimal context graph schema for a real-world workflow.
* Ingest and normalise data into a structured representation.
* Expose the context layer via MCP servers.
* Enable agents to query and update context through MCP tools.
* Persist reasoning state across sessions and agent boundaries.

By the end of the session, attendees will have built a working foundation for a context engine and understand how to evolve it into a shared context layer for multi-agent orchestration.

The default AI stack today ships user data to a cloud endpoint and hopes for the best. But regulations like GDPR, India's DPDP Act, and sector-specific mandates (healthcare, finance, defense) increasingly demand that sensitive data never leave the device or jurisdiction. This talk presents a working architecture that squares the circle: run frontier-class SLMs (Phi-3 Mini, Gemma 2B) locally via Meta's ExecuTorch runtime on Android, while using Anthropic's Model Context Protocol (MCP) as the orchestration layer that decides what runs where, which tools the model can access, and what data governance policies are enforced — all without a single byte of PII hitting an external server.

When we tried to scale our multi-agent MCP system beyond a single node, the orchestrator broke immediately.

Each model running on a different machine means the orchestrator had to track which models it called, which responses arrived, and which were pending. The moment the orchestrator restarted, everything was gone. Models got called twice. Responses were lost. Consensus became inconsistent.

Turns out this is the sticky session problem. And the ecosystem is still figuring it out.

As SEP-1442 is moving MCP toward a stateless-by-default transport model. But most deployments still depend on session affinity. When you are orchestrating models across distributed nodes, that dependency breaks everything.

This talk covers what we learned designing around it:

- Why multi-agent MCP orchestration creates hidden session dependencies
- How to decouple consensus state from the orchestrator process using structured event logs
- Migration patterns that preserve workflow correctness without session affinity
- Stateless MCP is not just an infrastructure concern. It is what makes multi-agent consensus reliable at scale.