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Manish
Kavuturi
Generative AI Engineer
FedEx
Manish Kavuturi is a Generative AI Engineer at FedEx, where he works on building enterprise-grade AI systems for logistics intelligence. He develops production-ready GenAI platforms using LLMs, RAG architectures, and agentic workflows to enable natural language querying, document automation, and intelligent search across large-scale operational data. His work supports improved shipment visibility, root-cause analysis, and faster customer query resolution in complex logistics environments. With over six years of experience across AI engineering and backend development roles at companies including FedEx, Conduent, and Dell Technologies, Manish has built scalable data pipelines, FastAPI microservices, and cloud-native AI systems deployed on Azure and AWS. He specialises in combining retrieval systems, MLOps practices, and observability tooling to deliver reliable, high-impact AI solutions in production.
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29 October 2026 12:30 - 13:00
Panel | From GenAI to autonomous workflows: how teams are evolving their AI systems
A generative output that a person reads is easy to forgive. One that feeds the next step in a workflow is not. Most generative stacks were designed for one-off results: a prompt, a retrieval step, and an answer someone checks. Once those outputs start feeding other steps and triggering real actions, the prompting, retrieval and evaluation choices that worked in isolation start to break. So what has to change in the stack before it can carry real work? This session brings together practitioners who are rebuilding their generative stack around workflows. They'll cover where their original design stopped holding up and what they changed. Through real examples and open disagreement, the panel will get into how prompting, retrieval and evaluation shift when outputs become dependencies, and how they decide when a system is ready to act without a person checking every step. Key takeaways: → Where stacks built for one-off results break once outputs feed other steps → How to design prompting, retrieval and evaluation for outputs that trigger actions → What to put in place before letting a system act on its own The questions still line up with the new takeaways: