MCP Dev Summit Mumbai 2026

MCP is the protocol layer for the new consumer of software. But protocols are necessary, not sufficient. The next generation of agents won't be soley defined by the protocol they speak — but also the infrastructure that powers it.

In this keynote, Sam Partee (CTO and Co-Founder of Arcade.dev) walks through the agent runtime components needed to drive more adoption of MCP: delegated identity that scopes what an agent can do; tool routing that survives ten thousand registered tools; memory and provenance that turns one-shot demos into auditable systems. Each layer is grounded in production data — what breaks, mistakes we made, what to build, why, and what the next two years of infrastructure work look like.

The Model Context Protocol (MCP) has fundamentally revolutionized developer velocity, allowing engineers to build and iterate complex AI agents seamlessly on a local machine. However, when moving these autonomous agents from a local sandbox into a production enterprise environment, organizations hit a sudden architectural ceiling. The local agility that fuels early-stage innovation quickly breaks under the compounding pressure of agentic sprawl, fragmented data integrations, and critical compliance requirements.

This keynote addresses the hidden architectural dimensions required to run enterprise AI agents safely and reliably at scale. Moving past individual agent scripts, we will explore why a centralized, fully managed platform is no longer a luxury, but a core system dependency. We will dive into the four essential pillars—Build, Scale, Govern, and Optimize—with a specific focus on how a managed MCP control plane provides the cryptographically-attested identity, granular gateway policies, and runtime isolation required to cross the chasm from an impressive local prototype to a secure, enterprise-grade AI ecosystem.

The LFX ecosystem faced fragmentation, hundreds of siloed APIs and scattered data made simple questions complex and prevented AI agents from connecting. This talk chronicles the journey to build the LFX MCP Server, a 100% open-source solution that established a single, standard AI interface, enabling every LFX system to speak the same language. You will hear why MCP was chosen over custom integrations and learn about unexpected challenges, including securing authentication, managing permissions, and tackling immediate data quality issues. Finally, we share critical lessons learned, emphasizing the power of fewer, sharper tools and the necessity of governance from day one.

Jupyter Notebooks are critical medium for code, data, and ML, demanding a paradigm shift for AI assistance. With Jupyter's real-time collaboration and cloud-native evolution, it's becoming a powerful portal to a full data platform, beyond mere notebooks.

This session explores MCP as the essential framework for human-AI synergy within this expanded Jupyter ecosystem. Leveraging Jupyter's extensibility, MCP expands its API, opening gateways to services across the entire data, ML, and AI landscape. By extending Jupyter’s real-time collaborative models, MCP enables AI agents to seamlessly co-create alongside human developers. This integration moves beyond traditional AI coding assistance, fostering true parallel work without conflicting edits, eliminating friction and accelerating development.

The speakers will give the live demo showing how MCP provides the blueprint for connecting AI assistance directly with the Jupyter environment, both locally and in the cloud. This empowers builders to redefine human-AI interaction and unlock unprecedented productivity across the entire AI development lifecycle – from data preparation and feature engineering to LLMs fine-tuning and evaluations.

Everyone's heard the AAIF framing: MCP handles agent-to-tool, A2A handles agent-to-agent. Clean in theory. In practice, every multi-agent system lives at the boundary of both, and that boundary has no standard.
Context gets dropped. Errors go silent. A state that survived an entire A2A task lifecycle vanishes the moment it crosses into an MCP tool call. Nobody is talking about this publicly.
This session replaces the "vertical vs horizontal" mental model with something more honest: a precise seam diagram showing what each protocol guarantees, what it doesn't, and what falls into the gap. We'll go deep on MCP's Tasks primitive (SEP-1686)- including its two admitted gaps: no retry semantics, no expiry policies. We'll map the real failure modes developers are hitting today, drawn from open GitHub issues across both protocol repos.
Then a live demo using Google's open-source A2A sample repo: no custom code. We'll run a real multi-agent task, cross the protocol boundary, sever the trace header, and watch the silent failure happen in real time.
You'll leave with a clear mental model, a three-point failure checklist, and a concrete picture of what the spec needs next.

At Motorola Solutions, downtime isn’t just an inconvenience—it’s a public safety risk where lives are at stake. This session explores how we revolutionized incident response for mission-critical applications by moving beyond fragmented tools and alert fatigue. Historically, siloed data across Kubernetes and Grafana led to 45-minute MTTRs and manual context-switching.

We will detail our adoption of the Model Context Protocol (MCP), specifically utilizing Red Hat Kubernetes and Grafana MCP servers to build an AI-assisted operations layer. Learn how this unified context allows an AI agent to correlate metrics with cluster states in seconds, reducing MTTR to under 4 minutes while sustaining 99.99% uptime.

We'll share our production architecture for natural language incident resolution and our "human-in-the-loop" approach that ensures SREs remain in control of all write operations. Attendees will leave with practical guidance on scaling reliability in regulated environments and empowering junior engineers with senior-level operational context. Discover how to turn fragmented data into a mission-critical intelligence amplifier.

MCP brings structure and standardization to how LLMs interact with tools and resources—but that power comes with cost. We’ve built multiple MCP servers across different use cases, and not all of them should have existed.
This session distills hard-earned lessons from real-world implementations: where MCP created long-term leverage, where it introduced unnecessary complexity, and where a direct API call would have shipped faster and cleaner. We’ll share a clear evaluation checklist you can use before starting your next build—so you choose MCP intentionally, not reflexively.