Build Robust MCP: Evaluate & Observe in Real-Time
MCP is the future. Reliable MCP is your competitive advantage.
As AI applications move from prototype to production, real-time evaluation and observability become the make-or-break factors for reliability. In this session, we unpack how the Model Context Protocol (MCP) offers a standards-based blueprint for building a resilient framework that powers live evaluation pipelines and monitoring without overhauling your stack.
This webinar delivers a practical roadmap to architect MCP for instant feedback, surface issues on the fly, and embed continuous quality checks - all while accelerating iteration and reducing engineering overhead.
💡 What you’ll learn
Run sentiment, accuracy, and toxicity checks with simple MCP commands - no coding needed.
Observe AI behaviour and reasoning live, using plain-language prompts.
Generate tailored synthetic datasets on demand with a single description.
Manage datasets → upload, analyse, export - directly via natural language.
Activate safety filters across your system instantly with one MCP command.
Bottom line: Learn how to control complex AI workflows with conversational ease using MCP, while ensuring reliability and accuracy.
About the Speakers
Nikhil Pareek, Founder & CEO of Future AGI, is a serial entrepreneur with over nine years of experience building startups and leading AI-driven innovation across industries like healthcare, IoT, consulting, and finance. He’s passionate about bringing engineering rigor to AI systems and solving core infrastructure challenges in model development and deployment. At Future AGI, he’s focused on helping teams build trustworthy, production-ready AI through automation, evaluation, and observability.
Rishav Hada, an Applied Scientist at Future AGI, specializes in AI evaluation and observability. Previously at Microsoft Research, he developed frameworks for generative AI evaluation and multilingual language technologies. His research, funded by Twitter and Meta, has been published in top conferences like EMNLP, ACL, and NAACL and integrated into AI products. His recent work on mitigating bias in language technologies won the Best Paper Award at FAccT’24.
About Future AGI
Future AGI is a San Francisco-based advanced AI Lifecycle Management platform designed to streamline experimentation, evaluation, and real-time observability. Traditional AI tools often rely on guesswork due to gaps in data generation, error analysis, and feedback loops. Future AGI eliminates this uncertainty by automating the data layer with multi-modal evaluations, agent optimisations, observability, and synthetic data tools, cutting AI development time by up to 95%. By removing manual overhead, it brings software engineering rigour to AI, enabling teams to build high-performing, trustworthy systems faster.
🌐 To know more about Future AGI, visit here!
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