Padmanabham Venkiteela
Senior Enterprise Architect-Integrations | Enterprise Integrations, API Management, Agentic AI | AI, SAP BTP, Boomi, GCP, Apigee, Oracle SOA, OIC | Cloud Transformation | AI/ML
Dallas, Texas, United States
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I am a Senior Enterprise Integration Architect with extensive experience leading the design and implementation of enterprise-grade integration platforms across diverse industries. My expertise lies in building scalable, secure, and efficient integration solutions using modern system architecture strategies and patterns and API-driven models.
With hands-on experience in leading technologies such as Boomi Integration & API Management, SAP BTP (Business Technology Platform) , Google Apigee X, Oracle Integration Cloud (OIC), Oracle SOA Suite (11g & 12c), B2B, BPM, WebLogic, Informatica Cloud, and CI/CD pipelines (Jenkins), I thrive across the full integration lifecycle—architecture, development, administration, performance tuning, and production support.
I hold multiple cloud certifications (Google Cloud, Oracle Cloud, AWS Architect, Multi-Cloud Architect) and several Boomi certifications (Professional Developer, API Designer, API Management, Application Architecture), Post Graduate Program in AI & ML in UTA , reflecting a strong foundation in cloud-native , multi-cloud solutions and AI.
Over the years, I have architected and delivered complex enterprise integrations for platforms such as SAP, Salesforce (Sales & Service), CPQ, MDM, BigQuery, Oracle CRM Siebel, Oracle Fusion Applications, Revpro, Renewals, Adobe, Pardot, and various databases (MSSQL, Oracle DB, Autonomous DB, Cloud SQL, Postgres). My portfolio also includes technical HR integrations for Workday, Ariba, Concur, Fieldglass, ServiceNow, SABA, ADP, Hyperion, Okta, WorkInSync, and more.
I bring deep domain knowledge of end-to-end business processes—Hire to Retire, Order to Cash, Quote to Order, Record to Report, Source to Pay, Procurement, Sales & Support, and MDM—and have successfully integrated global financial systems with AMEX, Citibank, Chase, BOA, HSBC, and Thomson Reuters/Refinitiv.
A strong collaborator, I work closely with project managers and cross-functional teams to plan, guide integration strategies, and manage dependencies. I have also driven data center workload migrations and cloud transformations, transitioning enterprise systems from on-premises to AWS and GCP.
In addition, I am actively exploring AI Agent development and applied AI solutions. I have been building enterprise AI agents to streamline workflows, automate decision-making, and integrate AI into large-scale business processes—bridging my deep integration background with emerging AI innovations.
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IEEE-International Conference of Data Analytics for Sustainability and Engineering Technology(DASET)
How Artificial Intelligence (AI) and Machine Learning (ML) can be implemented in the context of middleware platforms to increase the connectivity of the enterprise, specifically SAP Business Technology Platform (BTP) and Boomi. The study looks at the optimization of data flow, automation of integration processes and enhances cross-application interoperability in intricate enterprise cosystems using AIenhanced middleware. Conventional middleware is usually manually configured and not adaptively intelligent and therefore the efficiency in the data synchronization and process orchestration. With integrated ML algorithms, middleware can dynamically predict integration patterns, map automatics, anomaly detection, and heal workflows. The paper evaluates architecture archetypes in which AI-based automation is used to enable real-time decision-making and operational scalability between the intelligent suite of SAP BTP and the Boomi low-code integration environment. Comparison analysis
reveals quantifiable changes in integration latency, system uptime and accuracy of data. There is also the AI-based middleware that improves governance by predictive monitoring and automatic resolution of errors. The results highlight that AI and middleware convergence bring a paradigm change in terms of autonomous integration systems, which offer shorter development time and lower operation expenses. The research findings show that AI-enriched middleware is a useful strategic catalyst of digital transformation, which guarantees agility, resilience, and smart orchestration in hybrid enterprise environments.
World Congress on Smart Computing (WCSC2026)
Smart Computing is rapidly shifting from automation to autonomous, goal-driven digital ecosystems. This keynote explores how Agentic AI, multi-cloud architectures, and unified interoperability frameworks are transforming enterprise operations. Drawing from large-scale real-world implementations, including SAP BTP modernization, Opportunity-to-Order (O2O) transformation, and vendor-agnostic integration architectures, this talk presents a practical blueprint for building intelligent, adaptive, and self-orchestrating enterprise systems. Key topics include multi-agent coordination, Model Context Protocol (MCP) enabled interoperability, cross-cloud API governance, federated data pipelines, and AI-augmented decision workflows. The session highlights how enterprises can responsibly adopt autonomous AI to enhance operational efficiency, accelerate digital transformation, and build scalable, future-ready ecosystems across AWS, GCP, Azure, OCI, and SAP platforms.
International Conference on Innovations in Computing, Automation, Engineering, and Applied Sciences
Agentic AI represents the next major evolution in enterprise automation, moving organisations
beyond traditional rule-based workflows toward autonomous, goal-driven systems capable of
reasoning, planning, and acting independently. This keynote explores how agentic
architectures, powered by Large Language Models and the emerging Model Context Protocol
(MCP), are redefining multi-cloud orchestration, enterprise integrations, and cross-system
decision automation. The Enterprise Agentic Architecture Framework (EAAF) is introduced as
a holistic model that unifies agent layers, secure tool-use, interoperability, integration
platforms, governance controls, and continuous feedback loops. Real-world scenarios
including SAP BTP-Boomi-Salesforce flows, multi-agent collaboration, and autonomous
cloud optimisation across AWS, Azure, GCP, and OCI illustrate measurable gains in efficiency,
resilience, and operational scalability. The session also highlights strategic trends toward selfhealing
systems, autonomous supply chains, and enterprise-wide agent marketplaces,
positioning agentic AI as a transformative force for the next decade of digital innovation
2nd International Conference onSustainable Computing and Intelligent Systems (SCIS 2025)
Contemporary enterprise financial functions, especially procurement and accounts payable (AP), struggle significantly with data fragmentation. Core financial transaction details are scattered across disparate platforms such as SAP ECC, SAP Ariba, and Business Warehouse (BW) making it slow and difficult for teams to acquire a complete, accurate understanding of a purchase order or invoice. This fragmented landscape necessitates reliance on specialized SAP expertise, which results in persistent operational delays and a dependence on support teams. To overcome this challenge, we introduce the Natural-Language Procurement Data Assistant, an enterprise-grade AI Agent engineered to provide real-time, contextually accurate answers to procurement queries. The solution is founded on a secure, scalable, and API-first architecture, with its core innovation being a Retrieval-Augmented Generation (RAG) pipeline utilizing LangChain/LangGraph for advanced, multi-step reasoning. This agent directly translates natural language questions into optimized SQL queries, which are then executed against a centralized Enterprise Data Platform (EDP), hosted on Google BigQuery. By establishing the EDP as the single source of truth that unifies all SAP and Ariba data, the system guarantees accurate, consistent, and auditable responses. The agent is deployed within a containerized Google Kubernetes Engine (GKE) environment, interfacing with the Trellix Hopper Gen AI front-end. Crucially, Google Apigee X is integrated to provide the necessary API management and security, enforcing enterprise-grade authentication, throttling, and access policies. Empirical testing on live procurement scenarios demonstrates significant operational benefits: the system achieves near-perfect accuracy (98–100 percent), maintains a rapid average query latency of less than 4 seconds, and reduces the AP team workload by a substantial 60 percent. This integrated architecture serves as a robust reference model for deploying secure, explainable, and compliant AI agents within complex enterprise procurement ecosystems, effectively transforming conversational AI into a tool for real-time operational intelligence.
IEEE-Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF 2025)
A Federated AI Framework has been introduced to support a secure multi-cloud enterprise integrations in the response to the challenges of AI-driven system data privacy, scalability, and performance. The framework enables training of decentralized models on a variety of cloud platforms (Hybrid Federated Learning (HFL) on multiple cloud platforms: AWS, GCP, and Azure) and protects the privacy of data with the Intel SGX and differential privacy techniques. The assimilation of enterprise systems is supported with the help of such tools as SAP BTP, MuleSoft, and Apigee, which guarantee the smooth exchange of data and safe administration of APIs in the heterogeneous cloud systems. Intel OpenFL is used to coordinate the process of federated learning in order to optimize the model accuracy and decrease the communication latency. This solution allows organizations to use the maximum potential of multi-cloud ecosystems without risks in security or compliance, which is why it can be considered the most appropriate solution when implementing AI models in regulated industries. As the experimental results show, the model accuracy, training performance and privacy guarantees have improved significantly.
ICAEAS 2025 - International Conference on Multidisciplinary Research & AI for Sustainability
API Management platforms are central to digital transformation, enabling enterprises to innovate faster, integrate seamlessly, and deliver secure, scalable services. As organizations modernize their legacy middleware systems, choosing the right API management solution is a critical decision that affects both technical agility and business outcomes. This keynote will provide a comparative look at leading API Management platforms, focusing on SAP BTP Integration Suite, Google Apigee X, MuleSoft, Boomi, and Oracle. The session will evaluate these platforms based on dimensions such as API lifecycle management, governance, developer productivity, cost optimization, multi-cloud flexibility, and security. The talk will highlight emerging trends like AI-driven API ecosystems, intelligent automation, and sustainable architectures that support enterprise growth and resilience. By examining real-world case studies and industry best practices, this keynote will give participants a structured framework to make informed decisions on API modernization, avoid common pitfalls, and unlock long-term digital value.
Padmanabham Venkiteela
Senior Enterprise Architect-Integrations | Enterprise Integrations, API Management, Agentic AI | AI, SAP BTP, Boomi, GCP, Apigee, Oracle SOA, OIC | Cloud Transformation | AI/ML
Dallas, Texas, United States
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