AI in finance is no longer about automating isolated tasks, it’s about designing autonomous agents that can reason, retrieve, and act across complex, high-stakes workflows. Finance systems demand more than chat: they require decision-grade intelligence that is secure, compliant, and scalable.

For this session, we’ll explore how SOTA agentic AI is reshaping financial operations using Azure AI. Traditional rule-based systems often fail when workflows involve uncertainty, causal reasoning, or integration across multiple data sources. Agentic workflows, powered by large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration, succeed where traditional systems fall short.

We’ll examine patterns and architectures for building finance-grade AI agents on Azure AI, highlighting how they can:
- Automate document-heavy processes like policy reviews, KYC, and regulatory reporting.
- Support risk assessment and decision-making by combining structured and unstructured data.
- Deliver trustworthy, compliant workflows through responsible AI guardrails.

By the end of this session, participants will be able to:
- Design finance-grade agentic workflows using Azure AI, RAG, and multi-agent orchestration.
- Implement practical use cases such as KYC automation, policy document analysis, and compliance reporting with LLM-powered agents.
- Apply responsible AI principles to ensure trust, compliance, and scalability in financial systems.

This session will mix real-world case insights with practical demos, I will be unpacking my production eperience showing how financial organizations can go beyond chatbots and move towards enterprise-ready intelligent agents. Whether you’re a developer, architect, or decision-maker, you’ll leave with actionable strategies to start building your own agentic workflows for finance systems on Azure AI.