Nivedita Dixit
Infosys Ltd, AVP - Senior Principal Technology Architect
Hyderābād, India
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AVP, Senior Principal Tech architect, leading commerce architecture practice in DX unit of Infosys, has driven numerous large scale digital transformation programs across the diverse sectors. Brings deep tech expertise & leads the commerce architecture practice providing technical advisory. Built solutions on Agentic Commerce, Agentic AI and having certifications in these emerging areas. Published multiple white papers on Agentic commerce leveraging ACP, Google UCP, MCP, AP2 & etc.
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Enterprise‑Grade MCP Architectures: Beyond the Happy Path
As MCP moves from experimentation into enterprise adoption, teams quickly encounter challenges that go beyond happy‑path examples. MCP's primitives are elegant. Production MCP systems are not. This session dissects the architectural decisions that separate a functional prototype from an enterprise-grade deployment, one that survives adversarial inputs, organizational scale, and operational scrutiny. Provides a deep architectural dive into designing enterprise‑grade MCP systems, focusing on protocol behavior in real‑world conditions such as scaling, partial failures, transport choices, compatibility, and governance. The session will detail how MCP’s layered architecture behaves under load and change, and highlight patterns that enable resilient, secure, and evolvable MCP deployments in complex, multi‑server environments.
Designing MCP‑First AI Applications: Practical Patterns for Building Scalable, Tool‑Driven Agents
As organizations move from LLM experimentation to real AI deployments, teams face a familiar tension: delivering quick, tangible value without building brittle architectures that are hard to evolve.
The Model Context Protocol (MCP) offers a powerful abstraction for tool‑driven agents, but its full impact is realized only when applications are intentionally designed for both rapid production enablement and long‑term maintainability.
Drawing from real‑world platform and solution‑design experience, this talk explores practical patterns for building MCP‑first AI applications that reach production quickly without sacrificing architectural integrity. It demonstrates how clean separation of model behavior, tool execution, and domain logic enables teams to ship AI agents faster while preserving extensibility. The session also shows how MCP servers can act as stable, reusable contracts that accelerate initial delivery and support iterative evolution.
Attendees will leave with pragmatic patterns for structuring MCP tools, minimizing prompt complexity, and avoiding common design pitfalls that slow production rollout, along with strategies to evolve a working solution into a scalable agent p/f
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