Jay Shukla
Student at Indian Institute of Information Technology, Nagpur
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Jay Shukla is a CSE (AI/ML) student at IIIT Nagpur and a Research Intern at SVNIT Surat, working on energy time forecasting using deep learning. He is skilled in Python, TensorFlow, and Generative AI, and is also exploring Reinforcement Learning, with experience in building AI models across domains. He has also led hackathons and mentored 60+ students in applied machine learning and data science.
The coordination tax: why your MCP multi-agent system degrades at scale, and how to fix it
Multi-agent MCP systems work beautifully in staging. They fail in production. We learned this the hard way: three agents, nine tools, accuracy that quietly degraded under real load, and a job that blew past its token budget before anyone noticed.
We weren't alone. Google DeepMind and MIT's December 2025 paper "Towards a Science of Scaling Agent Systems" measured up to 17× error amplification in naive multi-agent setups and found coordination yields negative returns past a saturation threshold. Separate work (MAFBench, 2025) shows framework design choices alone can cut planning accuracy by 30% and collapse coordination success from over 90% to under 30%. Most MCP deployments hit this wall and misdiagnose it as a model problem.
This talk walks through three failure modes - Infinite Loop, False Consensus, Silent Fallback with message traces, token costs, and detection times. We then introduce the "topology contract": a lightweight JSON schema embedded in MCP server metadata, compatible with the 2026 Server Cards roadmap. Additive to the spec, zero protocol changes.
Attendees leave with a reproducible benchmark suite and a schema they can adopt in an afternoon.
MCP Dev Summit Mumbai 2026 Sessionize Event Upcoming
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