Nithesh Gudipuri
Associate Director, Technology Architecture & Modernization | AI & Data Strategy | Blockchain | IEEE Published Author | Speaker • Advisor • Industry Contributor
Tampa, Florida, United States
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Nithesh Gudipuri is Associate Director of Technology at Raymond James Financial, with 13+ years across financial services, telecommunications, and healthcare. He modernized 100+ services supporting $1.3 trillion in assets, holds two USPTO patents, and leads SEC regulatory implementations ( T1 Settlement, USTC). Named Innovation Champion (top 1% of 1,800 technologists), he advises on blockchain strategy and has presented research at IEEE/ACM conferences.
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Your Agent Has a Credit Card Now: Zero-Trust Patterns for Agentic Commerce
*Applicable to everyone who shops online*
AI agents are about to start spending real money on behalf of real users — and most of our security models weren't built for it. When an agent browses a product page, reads a review, and clicks "buy," who is the principal? Who authorized the spend? What stops a prompt-injected review from draining a user's card?
This session walks through zero-trust patterns for agentic commerce: scoped and short-lived credentials, capability-based tool access, spend caps and velocity limits, intent verification, and audit trails that survive an LLM's account of what happened. We'll look at real threat models — confused deputies, prompt injection via product data, runaway autonomous spend — and the emerging standards (OAuth token exchange, Stripe and card-network agent payment specs, MCP authorization) developers can build on today.
ReconGraph: Agentic AI for Real-Time Trade Break Discovery in Enterprise Securities Operations
What happens when an agentic AI system is responsible for resolving trade breaks across 103 million daily transactions — with zero tolerance for error and a T+1 regulatory clock ticking?
This session introduces ReconGraph, an agentic graph-reasoning framework purpose-built for post-trade reconciliation in enterprise securities operations. Unlike traditional rule-based exception management, ReconGraph deploys autonomous AI agents that traverse a live knowledge graph of trades, counterparties, custodians, and settlement states — discovering break patterns, classifying root causes, and recommending resolution pathways with full audit traceability.
Attendees will walk through the architecture of a production-grade agentic system operating under FINRA and SEC compliance constraints, including how agent decision boundaries are enforced, how human-in-the-loop escalation is triggered, and how every agent action is logged to satisfy regulatory audit expectations. This is not a proof-of-concept — it is a practitioner's blueprint from the front lines of T+1 settlement modernization.
Key takeaways: graph-augmented agent design patterns for financial workflows, compliance-aware agent boundaries, and lessons from deploying autonomous AI in zero-downtime fault-tolerant infrastructure.
From Ethics to Control: Building Auditable AI Agents for Regulated Financial Enterprises
Every financial firm is deploying AI agents. Very few can answer the question regulators are starting to ask: "How do you know what your agent did — and why?"
This session presents the Ethics-to-Control Trace Graph (ECTG), a governance architecture designed specifically for agentic AI operating in regulated financial environments — wealth management, capital markets, and enterprise securities operations. ECTG creates a live, executable mapping between AI ethics principles and operational control points, transforming agent governance from policy documents into auditable runtime behavior.
Drawing on research aligned with the emerging IEEE 3410-2025 Model Risk Management standard for GenAI in finance, this talk demonstrates how enterprise architects and AI engineers can instrument their agent pipelines for explainability, control, and regulatory defensibility — without sacrificing the autonomy that makes agents valuable.
Attendees will leave with: a working ECTG design pattern, a checklist for audit-ready agent deployments, and a framework for mapping agent actions to SEC/FINRA compliance obligations. Built for practitioners who are shipping agents into production, not just theorizing about them.
Building for the Agent Shopper: What Changes in Your Stack When AI Agents Buy
*Applicable to everyone who shops online*
Commerce platforms were built for humans — browsing products, comparing options, and completing purchases manually. But as AI agents evolve into autonomous decision-makers, enterprises must rethink how digital commerce systems are designed and governed.
This session explores the rise of the “agent shopper” — AI systems capable of discovering products, evaluating options, and making purchasing decisions on behalf of users or businesses. Attendees will learn how enterprise architectures must evolve to support agent-driven commerce through intelligent APIs, event-driven workflows, secure integrations, and real-time decisioning.
The talk also covers governance, trust, fraud prevention, and scalable architecture patterns needed to build secure, AI-ready commerce ecosystems for the next generation of digital interactions.
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