HALL 1 (Main) - Keynotes / Tech Talks

We will move past the hype of generative AI and dive into the technical frameworks that allow machines to navigate the real world with intelligence and autonomy.

Everyone is building agents. Almost no one is running them in production.

Eighteen months ago, one of our agents changed a VLAN configuration on a network device during a controlled proof of concept. It put an access point on the wrong VLAN, isolating every client connected to it. The agent completed the task successfully. No error was thrown. No alert fired. From the system’s perspective—everything was fine.

That incident taught us more about production-grade agentic AI than any benchmark or architecture paper. It revealed four failure zones that most enterprise AI teams have not yet engineered for: Silent Failure, Black Box Decisions, Permission Explosion, and Runaway Execution.

This session moves beyond agent architecture theory to the operational discipline required to run autonomous systems inside mission-critical enterprise environments—the kind where SLAs are real, permissions matter, and a wrong decision has consequences. We will walk through the 4-Layer Enterprise Agent Stack, a framework built on the principle that “The user is all”—ensuring agent permissions never exceed what the human behind the request is authorized to do.

The future of enterprise AI will not be decided by model intelligence. It will be decided by operational discipline.

Building an agent can be done quickly. Building an agent that holds up in production, under time pressure, with operational reliability – now, that’s the hard part.

In this talk, an AI researcher from Millennium will explain a practical engineering playbook for deploying agents into high-stakes buy-side workflows: research, monitoring, operations, and reporting processes.

This talk will go beyond buzzwords to show how to build a production-grade agent: composing proven patterns (with tool-chaining, reflection, human-in-the-loop, and selective multi-agent design) into systems that are constrained, observable and governable. This session will highlight the failure modes that tutorials omit, and the design decisions that prevent them.

BharatGen represents a new paradigm in building AI systems that are sovereign, inclusive, and purpose-built for India’s diverse linguistic and cultural landscape. This session explores how frugally scalable multilingual and multimodal AI models can be developed to serve Bharat at scale, balancing technological advancement with accessibility and efficiency. It will highlight the principles behind shared national AI infrastructure, enabling collaboration across academia, industry, and government to create AI that understands and serves India’s many languages and modalities. Attendees will gain insights into the opportunities, challenges, and impact of building sovereign AI capabilities that empower innovation while ensuring that AI development remains accessible, affordable, and aligned with the needs of Bharat.

As AI systems evolve from simple prompt-response models to autonomous, goal-driven agents, evaluating their performance becomes significantly more complex. This session explores the emerging challenges and methodologies for assessing Agentic AI systems, moving beyond traditional prompt accuracy metrics toward holistic evaluation of task completion, reasoning reliability, tool usage, and real-world effectiveness. It will discuss practical frameworks, benchmarks, and evaluation strategies that help measure how well AI agents plan, adapt, and execute multi-step tasks. Attendees will gain insights into building robust evaluation pipelines that ensure agentic systems are reliable, accountable, and ready for deployment in real-world applications.

AI adoption in software organisations is not failing only because the capability of models are insufficient. One of the reasons is the teams using them have not developed the mindset, habits, or knowledge structures needed to unlock its full potential.

This talk makes the case that the primary bottleneck is disposition— the posture with which a practitioner approaches AI. The talk is grounded in a production-tested framework and inspired from the collective intelligence of individuals who have applied techniques to achieve 100% AI-augmented code generation on live projects for over a year.

The talk introduces a blueprint in which domain experts encode structured knowledge that is leveraged by AI to generate code that enables the organisation to scale and grow at a much faster pace.

The audience after the session will leave with understanding of why AI seems challenging to adopt in domain-specific contexts, what structured knowledge encoding looks like in practice, and one actionable step they can take in their own team the following week.

Most AI initiatives fail not because the model is weak, but because teams lack a shared, reusable pattern language that turns experimental wins into production systems.This session distils three decades of engineering into 8 eras and 300+ patterns — showing how each technology wave (structured programming, OOP/GoF, SOA/events, cloud/microservices, cloud security, AI/ML, and now agentic AI) accelerated once solutions were named, standardised and made communicable.
Attendees will learn the core pattern families behind production-grade agentic systems: reasoning, memory (RAG), tool use (ReAct), orchestration and enterprise safety controls including human-in-the-loop gates. The session concludes with a live Spec-Driven SDLC demo — where the spec acts as the contract coordinating a multi-agent delivery pipeline, from architecture through deployment. The talk closes by connecting the methodology to BITS Pilani Digital’s AI Engineering & MLOps programmes, demonstrating how industry–academia partnerships enable learners to apply these patterns to real-world problems and move from prototypes to production with engineering rigour.

As AI agents evolve from experimental prototypes to real-world production systems, enterprises are realizing that model capability alone is not enough. The true challenge lies in how agents manage, retrieve, and retain information across complex, long-running workflows. Getting this right is often the difference between an impressive demo and a system that consistently delivers value.
Building effective agentic AI requires a strong approach to context and memory. Developers must understand how different memory types such as in-context, external, episodic, and semantic work together, and when to use each. Techniques like Retrieval-Augmented Generation (RAG) help bridge knowledge gaps, while thoughtful design ensures agents can retain and use information across sessions.
Join us for this Tech Talk where Sanketh and Anshul will break down how context and memory shape intelligent agents. Walk away with a practical framework for building AI agents that remember, reason, and scale reliably.

In an age that rewards urgency, Cheteshwar Pujara chose endurance.

This conversation traces a career built not on flourish, but on resolve — from disciplined beginnings in Rajkot to defining performances in Australia, from absorbing pressure at Brisbane to beginning again in county cricket. At No. 3, he learned to walk in early and leave late.

We’ll explore concentration, doubt, reinvention, and the craft of staying when the game — and sometimes the system — moves on.

An evening about patience as strength, time as an ally, and the quiet ambition required to hold your ground.

Generic AI coding assistants are great at syntax but fail at context. They don’t know your production schemas, your dbt DAGs, or your FinOps constraints. In this session, we dive into Cortex Code, Snowflake’s native AI agent that operates within your data’s security perimeter. We will demonstrate how Cortex Code moves beyond simple code generation to perform “agentic” tasks: self-healing data pipelines, automated dbt scaffolding, and cross-platform orchestration (CLI to Snowsight). Learn how to turn natural language into production-ready, governed data infrastructure in minutes rather than hours.