MCP Dev Summit Bengaluru 2026

MCP enables powerful AI-driven workflows — but production enterprise systems have SLAs, throughput contracts, and years of performance tuning. Wiring a reasoning loop into that environment reveals costs that don't show up in demos: latency amplification from sequential tool invocations, data movement overhead across system boundaries, schema drift, and observability gaps that span model reasoning and distributed backends.

This talk takes a systems-engineering lens to MCP in enterprise-scale data platforms. We formalize the MCP execution pipeline, identify where overhead accumulates, and present architectural patterns that contain the cost — including isolating orchestration from hot paths, fronting high-performance backends with thin MCP adapters over gRPC, and tracing multi-step workflows end to end.

The central argument: MCP belongs on the control plane, not the data plane. Enforce that boundary, and you gain adaptive orchestration without sacrificing reliability. Cross it, and predictability erodes fast. Attendees leave with a latency model, a failure-mode taxonomy, and a practical framework for deploying MCP in production — deliberately, not by accident.