Staging environment

Ship Production AI Agents: The ADLC Method in 30 Minutes

Hosted by Deepak Kumar

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What you'll learn

Map your SDLC to agentic workflows

Spot exactly where agents plug into requirements, design, build, test, deploy and ops

Design audit-ready agent workflows

Make every agent action traceable - the question your CIO will ask first

Right-size models to the job

When a small local model beats a frontier API on cost, latency and data privacy

Why this topic matters

Every team has an AI agent demo. Almost none have one in production. The gap is not the model - it is the missing lifecycle between a cool demo and a system your CIO approves. ADLC is that discipline, and this lesson gives you the map in 30 minutes.

You'll learn from

Deepak Kumar

Founder, DatavedamEdge | Ex-Twilio, Tellius, Zebra | NVIDIA Inception

I'm Deepak Kumar, founder of DatavedamEdge, an agentic AI studio where we build and run production AI agents for enterprise workflows, plus edge AI on Jetson-class hardware. I've spent 21 years leading data, AI, and platform teams at Twilio, Tellius, and Zebra, and now I train enterprise teams on ADLC - the Agentic Development Life Cycle, a method I developed for shipping agents that survive production: right-sized models, audit trails, and evaluation gates at every stage.

I've taught this method to engineering and analytics teams across industries, and this course is the same playbook I run with my clients: no toy demos, just the working systems, failure modes, and decision frameworks that get agents from prototype to trusted deployment.

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