Archika Dogra

Roughly 80% of the context that powers your business—invoices, contracts, call transcripts, support tickets—lives in messy, unstructured data and documents. Turning that into a structured data layer is one of the biggest opportunities in your organization: it's what lets you build accurate and reliable agents and analytics on your enterprise's full context. However, when building these massive scale pipelines, going from prototype to production is harder than it looks. Accuracy, cost, and throughput stop being independent dials and become a three-way tradeoff you have to design around. In this session, we'll share the framework and the playbook for winning that tradeoff. We'll show you how to compose AI Functions—Databricks' research-backed, task-specific building blocks for intelligent document processing—into production-ready agentic workflows. You'll learn how to choose the right function for each task, iterate on quality effectively, and tune for cost and throughput at scale. Along the way, we'll demo new features across AI Functions that push what's possible. You'll walk away with a concrete decision framework for building large-scale agentic workflows over unstructured data,…

A huge portion of the data that runs your business doesn't live in clean tables — it lives in millions of documents: invoices, contracts, forms, and filings. This is some of the richest context your enterprise has, and today's agents finally have a shot at unlocking it. But there's a catch: agents can't actually read your documents. On our OfficeQA benchmark, even frontier agents score below 50% on real enterprise document tasks because they struggle to read them. Close that gap, and every agent, dashboard, and decision downstream gets sharper. In this session, we'll show how Document Intelligence on Databricks turns unstructured documents into accurate, governed insights that power more accurate agents and analytics. We'll walk through how to build intelligent document processing pipelines with Lakeflow and AI Functions purpose-built for document understanding — taking your documents from raw PDF to rich answers in Genie. Walk away knowing how to turn your document corpus into the data layer that powers smarter, faster, and more trustworthy agents and analytics.

Ever struggled with getting AI to work effectively for your specific domain needs? Join us to discover how establishing the right data foundation transforms the way you build and deploy specialized AI agents. This session demonstrates how to prepare and structure your information assets to enable more powerful, efficient AI applications. Learn proven approaches for extracting value from both structured and unstructured data, creating knowledge bases that serve as the backbone for domain-specific agents, and implement optimization techniques that balance quality and resource constraints.Key takeaways: - Preparing diverse data sources for AI consumption and knowledge extraction - Building scalable data foundations that naturally facilitate agent development - Optimizing domain-specific agents for performance and cost efficiency - Implementing practical governance frameworks for AI systems built on your data

Whether you're using OpenAI, Anthropic or open-source models like Meta Llama, the Mosaic AI Gateway is the central control plane across any AI model or agent. Learn how you can streamline access controls, enforce guardrails for compliance, ensure an audit trail and monitor costs across providers — without slowing down innovation. Lastly, we’ll dive even deeper into how AI Gateway works with Unity Catalog to deliver a full governance story for your end-to-end AI agents across models, tools and data. Key takeaways: - Centrally manage governance and observability across any LLM (proprietary or open-source) - Give developers a unified query interface to swap, experiment and A/B test across models - Attribute costs and usage to teams for better visibility and chargebacks - Enforce enterprise-grade compliance with guardrails and payload logging - Ensure production reliability with load balancing and fallbacks