Day 2

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.

I’ll share insights on leveraging advanced AI to improve product quality, optimize process efficiency, reduce equipment downtime and increase yield. Looking forward to connecting with the AI developer community and sharing practical perspectives at MLDS 2026.

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.

Agentic AI is moving beyond “better prompts” into a new layer of infrastructure: standardized tool connectivity, agent-to-agent interoperability, and disciplined context engineering. In this session, I’ll share a production blueprint for building agents that can safely plug into enterprise systems using open protocols (MCP for agent-to-tools, A2A for agent-to-agent collaboration). We’ll cover how to design “context interfaces” (what the model can request, when, and why), handle long-running tasks with progress + recovery, and ship with strong authorization, auditability, and guardrails. Attendees will leave with a practical reference architecture and implementation checklist they can apply immediately.