Yesha Patel
Enterprise Solution Architect | AI-Driven Commerce & Customer Transformation @ IBM
Tampa, Florida, United States
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Yesha Patel is an Enterprise Solution Architect at IBM specializing in AI-driven commerce, leadership, and business transformation. She leads enterprise-scale digital transformation initiatives focused on modern commerce platforms, cloud-native architectures, customer experience, and intelligent digital ecosystems.
Her expertise spans enterprise architecture, strategic solutioning, API-first ecosystems, and large-scale transformation programs across Retail, Financial Services, Healthcare, and Manufacturing. Beyond client delivery, Yesha actively contributes to the technology community as a speaker, mentor, judge, and IEEE volunteer focused on AI, innovation, leadership, and digital transformation.
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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.
From AI Side Project to Production
From AI Side Project to Production: How to Actually Scale GenAI Applications
Everyone has built a ChatGPT wrapper. Few have scaled one.
In session, we’ll break down what separates AI demos from production-ready systems. You’ll learn:
- Why most AI pilots fail to scale
- How to design a scalable GenAI architecture (APIs, orchestration, observability)
- Cost control strategies for LLM-based applications
- Governance, security, and compliance considerations
- Measuring ROI beyond "it sounds cool"
Using real-world commerce and enterprise use cases, we’ll walk through how to move from hackathon-grade prototypes to durable, enterprise-grade AI platforms.
Perfect for developers building AI products, founders integrating LLMs, and engineers modernizing legacy systems.
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.
Architecting AI-Ready, Event-Driven eCommerce with Secure Microservices at Scale
Modern digital commerce is no longer just transactional — it is intelligent, distributed, and real-time. Retail organizations are re-architecting legacy monoliths into API-first, event-driven ecosystems capable of supporting AI personalization, dynamic pricing, and omnichannel fulfillment.
In this session, we will explore best practices for designing cloud-native eCommerce platforms using domain-driven microservices, asynchronous messaging, and zero-trust security principles. Drawing from enterprise retail modernization programs, this talk will cover:
Decomposing monolithic commerce platforms into bounded contexts
Designing scalable REST, GraphQL, and event-stream APIs
Implementing resilient service-to-service communication
Embedding AI capabilities into commerce workflows without compromising latency
Securing APIs with OAuth2, tokenization, and runtime threat detection
Observability strategies for distributed commerce systems
Attendees will leave with architectural blueprints and practical patterns to build AI-ready, secure, and highly scalable commerce platforms that operate reliably during peak demand events.
Why this works:
Strong alignment with microservices + AI + cloud-native modernization — high relevance to 2026 audience.
Yesha Patel
Enterprise Solution Architect | AI-Driven Commerce & Customer Transformation @ IBM
Tampa, Florida, United States
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