Staging environment
Free Lesson

Your AI Agent Works for 10 Users. What Happens at 10,000?

Hosted by Dr Ankur Narang, Kush Khurana, and Madhusudan Kumar

In this video

What you'll learn

Identify Where Agent Systems Break at Scale

Understand model limits, tool bottlenecks, database contention, queue buildup, state-mgmt issues and cascading failures

Design Scalable Agent Execution Architectures

Use queues, asynchronous workers, parallel execution, state stores & distributed orchestration to scale agent workflows

Control Latency, Throughput and Cost

Apply batching, caching, model routing, concurrency controls & workload prioritization to keep economics sustainable

Build Backpressure and Failure Isolation

Prevent overload from cascading through the system using rate limits, circuit breakers and workload isolation

Why this topic matters

An agent that works well in a demo may fail completely under production load. As usage grows, teams encounter model rate limits, API throttling, queue buildup, database contention, workflow-state problems, long-tail latency and rapidly increasing inference cost. Scaling agents requires more than adding servers. It requires a production architecture designed for concurrency, backpressure, distributed execution, state, reliability and economics.

You'll learn from

Dr Ankur Narang

Dr. Ankur Narang brings 30+ yrs exp in AI, Tech across MNCs & many verticals

Dr. Ankur Narang is the Founder & CEO of DeepCoreX Labs and brings 30+ years of experience across AI, technology leadership, venture building, enterprise transformation, and applied R&D. He has held senior AI and technology roles across research labs, startups, and large enterprises, and has worked on AI systems, decision science, agentic workflows, and deeptech venture creation.

Kush Khurana

AI & ML leader; Venture Partner, DeepCoreX; Ashoka faculty

Kush Khurana is an AI/ML leader, Venture Partner at DeepCoreX, and Visiting Professor at Ashoka University. He has 12+ years of experience building production AI systems, leading technical teams, and shaping AI-native products. He works with founders and learners to turn frontier AI and deep-tech ideas into practical, scalable systems.

Madhusudan Kumar

BTech & MTech IIT Mumbai. AI Lead with solid Agentic Experience

Madhusudan Kumar is a high-impact Technology and AI Lead with an impressive academic and professional trajectory. An alumnus of IIT Bombay, Madhusudan pairs his elite educational background with seven years of deep technical experience, specializing in the development and deployment of sophisticated AI systems. He is recognized for his intellectual rigor and a meticulous approach to project execution, consistently transforming complex algorithmic concepts into high-performing, functional software.

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Build Production AI Agents for 10x-100x ROI
Dr Ankur Narang, Kush Khurana, and Dr Aveek Brahmachari
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