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.
Area of Expertise
Topics
Securing Agentic AI in the Cloud: Identity, Delegated Authority and Zero-Trust Execution
As AI agents begin to access enterprise data, invoke APIs, coordinate workflows, and take actions across cloud platforms, organizations face a new security problem: autonomous software actors operating with delegated authority.
This session presents a practical architecture for securing Agentic AI in cloud environments. It covers non-human identity, least-privilege access, delegated authorization, policy enforcement, tool governance, behavioral observability, human approval, and failure containment.
Attendees will learn how to separate probabilistic AI reasoning from deterministic security controls and how to apply Zero Trust principles when agents interact with APIs, SaaS platforms, enterprise services, and sensitive data. The session also explores practical patterns for detecting abnormal agent behavior, limiting blast radius, maintaining auditability, and governing autonomous actions across distributed cloud environments.
The goal is to provide security architects, cloud engineers, and technology leaders with a vendor-neutral framework for moving Agentic AI from experimentation into secure enterprise operation.
Modernizing Enterprise Java with AI: From Legacy Jakarta EE Applications to AI-Ready Platforms
Many enterprises want to adopt Generative and Agentic AI, but their most important business capabilities still live inside mature Java and Jakarta EE applications.
This session explores how AI can accelerate the modernization of enterprise Java without forcing a wholesale rewrite. It covers practical patterns for understanding legacy code, identifying service boundaries, extracting business rules, documenting dependencies, exposing capabilities through APIs, and introducing AI-assisted development and testing into existing delivery pipelines.
The session also examines how Jakarta EE applications can evolve incrementally toward cloud-native and AI-ready architectures while preserving security, transaction integrity, reliability, and established business behavior.
Attendees will learn how to combine AI-assisted software engineering with Jakarta EE modernization patterns to reduce technical debt, improve developer productivity, and prepare existing enterprise applications for future integration with intelligent agents and AI-driven workflows.
From GenAI Pilots to Production: Securing the Agentic AI Control Plane
Generative AI has moved quickly from experimentation to enterprise adoption, but the next stage is significantly harder: enabling AI agents to reason, invoke APIs, interact with enterprise systems, coordinate workflows, and take action within complex business processes.
Traditional enterprise architectures were not designed for autonomous software actors.
This session presents a practical, vendor-neutral framework for moving from isolated Generative AI pilots toward governed, observable, and production-ready agentic AI.
Drawing on lessons from mission-critical financial-services architecture, large-scale enterprise modernization, and applied work in agentic systems, the session introduces the concept of an Enterprise AI Control Plane: an architectural layer that connects models and agents with the enterprise controls required for secure and reliable operation.
Attendees will explore practical implementation patterns for integrating AI agents with existing APIs, microservices, event streams, data platforms, and legacy systems while preserving the architectural discipline required in regulated and high-availability environments.
The session will examine how organizations can:
Establish agent identity, delegated authority, policy enforcement, and least-privilege access.
Introduce human approval and escalation controls for high-risk or irreversible actions.
Implement observability across prompts, tool calls, workflows, decisions, and autonomous actions.
Evaluate agent behavior, reliability, and operational risk before production deployment.
Maintain semantic consistency when agents interact with fragmented enterprise systems and data.
Design resilient workflows that account for model failures, API failures, downstream outages, and uncertain agent behavior.
Integrate Generative and Agentic AI incrementally without requiring wholesale replacement of existing enterprise platforms.
Create clear governance boundaries between AI reasoning and enterprise execution.
A practical reference architecture will illustrate how models, agents, enterprise services, APIs, identity, policy, observability, security, data, and human governance can operate as a coordinated system.
The session will also address one of the most important architectural shifts created by agentic AI: enterprise APIs are no longer consumed only by applications and people. Increasingly, they will be consumed by autonomous agents acting on behalf of users, systems, and business processes. This changes how organizations must think about authentication, authorization, traceability, lifecycle management, operational resilience, and risk.
Rather than presenting another AI platform or model comparison, this session focuses on the architectural and operational capabilities enterprises need regardless of which models, clouds, or AI platforms they adopt.
Attendees will leave with three practical outcomes:
A reference architecture for connecting AI agents safely to enterprise systems, APIs, data, and workflows.
A governance model for agent identity, authority, observability, human oversight, and operational risk.
An incremental modernization approach for introducing agentic capabilities into existing enterprise environments without disrupting mission-critical systems.
The objective is to help technology leaders move beyond the question of “How do we use Generative AI?” toward the more important enterprise question:
“How do we allow intelligent systems to act within the enterprise while keeping those actions secure, governed, observable, resilient, and accountable?”
The session provides attendees with a practical architecture and decision framework they can apply when moving from AI experimentation toward production-scale Agentic AI.
From Legacy Code to AI-Native Software Factories: Modernizing Mission-Critical Systems with Governed
Modernizing large enterprise codebases is no longer simply a matter of rewriting legacy applications in newer languages or moving workloads to the cloud. The harder problem is preserving business behavior, operational resilience, integration contracts, security controls, and decades of embedded domain knowledge while dramatically accelerating transformation.
This session presents a practical architecture for building an AI-driven software modernization factory for large, mission-critical systems.
Drawing on real-world experience modernizing enterprise financial platforms and applied research in AI-assisted software engineering, the session explores how generative and agentic AI can be introduced across the modernization lifecycle—from code understanding and dependency discovery to transformation, behavioral validation, integration testing, and production readiness.
The session will cover practical patterns for:
using AI to analyze and document large legacy codebases;
extracting business logic, dependencies, interfaces, and operational behaviors;
creating semantic representations that preserve system intent across modernization;
combining deterministic engineering controls with LLM-assisted code transformation;
validating behavioral equivalence between legacy and modern implementations;
introducing human review and policy gates into AI-generated software changes;
integrating security, observability, testing, and traceability into the modernization pipeline; and
scaling modernization across multiple applications without creating a new generation of technical debt.
A key focus will be the shift from using AI as a coding assistant to treating AI as part of a governed software factory—where models, agents, engineering tools, testing systems, security controls, and human experts operate within a repeatable modernization workflow.
Attendees will leave with a reference architecture and implementation framework for applying AI to enterprise modernization while preserving the reliability, traceability, and architectural discipline required for critical systems.
From Enterprise Java to Agentic AI: Building Governed AI Workflows with Jakarta EE
Enterprise Java applications are increasingly expected to integrate with Generative and Agentic AI without sacrificing security, reliability, transaction integrity, or existing architectural investments.
This session explores how Jakarta EE applications can evolve into AI-enabled enterprise platforms by combining REST APIs, dependency injection, security, messaging, persistence, and cloud-native integration patterns with agentic workflows.
Attendees will learn practical patterns for connecting Jakarta EE services to LLMs and AI agents, exposing enterprise capabilities safely as tools, enforcing authorization and policy boundaries, integrating asynchronous workflows, and maintaining observability across AI-driven execution.
The session also examines how existing Java applications can be incrementally modernized for Agentic AI without wholesale rewrites, preserving proven enterprise services while introducing intelligent orchestration on top of them.
The goal is to show how Jakarta EE can remain a strong foundation for secure, governed, AI-enabled enterprise applications.
Enterprise Semantic Continuity: Preserving Business Intent Across AI-Driven Modernization
Large enterprise systems rarely fail modernization because organizations cannot rewrite code. They fail because the meaning embedded across applications, interfaces, workflows, data models, business rules, and operational processes becomes fragmented or lost during transformation.
As Generative AI increasingly participates in software modernization, this challenge becomes even more important.
This session introduces Enterprise Semantic Continuity: an architectural approach for preserving business intent as systems evolve across legacy platforms, APIs, microservices, cloud environments, data platforms, and AI-enabled applications.
Rather than treating modernization as a sequence of independent technology migrations, Semantic Continuity treats business meaning as a first-class architectural asset.
The session explores how organizations can identify, represent, and maintain relationships among:
business capabilities and domain concepts;
legacy programs and embedded business rules;
APIs, events, services, and integration contracts;
data definitions and canonical business models;
workflows and operational dependencies;
modernized applications and cloud services; and
AI agents that increasingly reason over and act upon enterprise systems.
Participants will learn how semantic models, knowledge graphs, metadata, API contracts, event schemas, and AI-assisted analysis can work together to create a persistent representation of enterprise intent.
The session will also examine the role of semantic continuity in AI-assisted modernization. Generative AI can accelerate code transformation, but without reliable semantic context it may reproduce implementation patterns while losing critical business meaning.
A semantic continuity layer can help organizations improve modernization traceability, impact analysis, AI grounding, interoperability, testing, and architectural governance.
Attendees will leave with a practical framework for moving from code-centric modernization to intent-centric modernization, where the objective is not merely to replace old technology but to preserve and progressively improve the business semantics that make enterprise systems work.
Enterprise Semantic Continuity for Agentic Systems: Keeping Context, Intent and Actions Aligned
As enterprises introduce AI agents across APIs, workflows, data platforms, and legacy systems, a new problem emerges: agents may have access to information without reliably understanding the business meaning behind it. This session introduces Enterprise Semantic Continuity as an architectural pattern for preserving intent across systems, services, events, policies, and autonomous workflows. It explores how semantic models, metadata, knowledge graphs, API contracts, and observability can help agents maintain context, reduce ambiguity, improve decision quality, and execute safely across complex enterprise environments.
Beyond Copilot Security: Governing Autonomous AI Agents Across Enterprise Cloud Platforms
Copilots primarily assist users; autonomous AI agents can act on their behalf. As agents gain access to cloud applications, APIs, enterprise data, workflows, and privileged tools, traditional application-security controls are no longer enough.
This session examines how enterprise security must evolve for autonomous AI. It presents practical patterns for non-human identity, delegated authority, least-privilege access, policy enforcement, tool governance, behavioral monitoring, human approval, and auditable execution across cloud environments.
Attendees will learn how to define boundaries between AI reasoning and authorized enterprise actions, control what tools an agent can invoke, detect abnormal behavior, reduce blast radius, and maintain traceability across multi-step autonomous workflows. The session will also explore how Zero Trust principles can be extended to agents operating across Microsoft 365, SaaS platforms, APIs, data services, and distributed enterprise systems.
The objective is to provide security and cloud practitioners with a practical framework for evolving from Copilot-era security controls to governance models designed for autonomous digital actors.
AI-Driven Modernization of Mission-Critical Systems: From Legacy to Governed Software Factories
Modernizing mission-critical systems is not simply a code-conversion problem. Enterprises must preserve business behavior, integration contracts, security controls, operational resilience, and decades of embedded domain knowledge while accelerating transformation.
This session presents a practical architecture for building an AI-driven software modernization factory for large and complex enterprise systems.
Drawing on experience with financial-services platforms, distributed architectures, and AI-assisted modernization, the session explores how Generative and Agentic AI can support the full modernization lifecycle: code understanding, dependency discovery, business-rule extraction, transformation, testing, behavioral validation, and production readiness.
Participants will learn practical patterns for:
analyzing and documenting large legacy codebases with AI;
extracting business logic, interfaces, dependencies, and operational behavior;
creating semantic representations that preserve system intent during transformation;
combining deterministic engineering controls with LLM-assisted code generation;
validating behavioral equivalence between legacy and modern implementations;
introducing human review, security, and policy gates into AI-generated changes;
integrating traceability, observability, testing, and quality controls into the modernization pipeline; and
scaling modernization across application portfolios without creating a new generation of technical debt.
A key theme is the transition from using AI as an individual coding assistant to treating AI as part of a governed software factory in which models, agents, engineering tools, testing systems, security controls, and human experts operate within a repeatable lifecycle.
Attendees will leave with a reference architecture and decision framework for applying AI to enterprise modernization while preserving the reliability, traceability, and architectural discipline required for critical systems.
The Agentic AI Control Plane: Identity, Policy, Observability and Safe Execution at Scale
Enterprises are moving from copilots to agents that call APIs, use tools, access data, coordinate workflows, and take actions. That shift creates a new infrastructure problem: how do we govern non-human actors without constraining useful autonomy? This session presents a vendor-neutral Agentic AI Control Plane for production systems, covering agent identity and delegated authority, policy-as-code, tool governance, behavioral evaluation, end-to-end observability, human approval, failure containment, and auditable execution. Attendees will see architectural patterns for separating probabilistic reasoning from deterministic enterprise controls and safely integrating agents with existing APIs, services, data platforms, and workflows.
The Smart City AI Control Plane: Governing Autonomous Agents Across Digital Infrastructure
Smart cities are increasingly becoming distributed digital systems.
Transportation networks, public services, utilities, emergency-response systems, IoT infrastructure, digital twins, data platforms, and citizen-facing applications are producing continuously changing streams of information. Artificial Intelligence is already being introduced across these environments for prediction, optimization, monitoring, and decision support.
The next evolution is Agentic AI: intelligent systems capable not only of analyzing information, but also of invoking APIs, coordinating workflows, interacting with digital infrastructure, and taking actions on behalf of people and organizations.
That shift introduces a fundamental architectural challenge:
How can cities allow AI systems to act across critical infrastructure without sacrificing governance, security, resilience, transparency, or human accountability?
This session presents a practical, vendor-neutral architecture for introducing governed Agentic AI into smart-city environments.
The session introduces the concept of a Smart City AI Control Plane—an architectural layer that connects AI models and autonomous agents with city data platforms, APIs, digital twins, IoT systems, enterprise services, identity, policy, observability, and human governance.
Participants will explore architectural patterns for:
connecting AI agents to smart-city APIs, IoT platforms, digital twins, data spaces, and existing enterprise systems;
establishing machine identity, delegated authority, least-privilege access, and policy enforcement for autonomous systems;
maintaining semantic consistency as agents interact with data produced by different agencies, systems, vendors, and infrastructure platforms;
observing and tracing agent decisions, tool calls, workflows, and actions across distributed urban systems;
introducing human approval and escalation controls for sensitive or high-impact decisions;
designing resilient agentic workflows that account for unreliable models, unavailable APIs, sensor anomalies, network failures, and downstream infrastructure outages;
evaluating autonomous-agent behavior before granting access to operational systems; and
incrementally introducing AI capabilities without replacing existing smart-city or municipal technology platforms.
A practical reference architecture will demonstrate how AI agents, digital twins, IoT infrastructure, urban data platforms, APIs, identity, cybersecurity, policy, observability, and human oversight can function as one governed system.
The session will also examine an important evolution in smart-city architecture: APIs and digital infrastructure will increasingly be accessed not only by people and applications, but by autonomous digital actors operating on behalf of transportation systems, utilities, government services, infrastructure operators, and citizens.
This changes the requirements for identity, authorization, interoperability, observability, cybersecurity, operational resilience, and accountability.
Rather than advocating a particular AI platform or model, the session focuses on architectural principles that can be applied across technology vendors and city environments.
Attendees will leave with a reusable framework for evaluating how Agentic AI can be safely introduced into smart-city ecosystems while maintaining the reliability and public trust required of critical digital infrastructure.
The objective is to help technology and public-sector leaders move beyond:
“Where can we use AI in a smart city?”
toward the more important systems question:
“How do we allow intelligent systems to act across city infrastructure while keeping those actions secure, governed, observable, resilient, and accountable?”
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.
AgentCon - Orlando Sessionize Event
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