Yesterday’s agile sprint asked, “What can we deliver in six weeks?” --The new SDLC asks, “What can human-AI agent teams safely design, build, test, secure, and release in six days?”

Core Thesis -- The future SDLC is not faster typing. It is faster convergence across all development stages. The value is not that AI writes code quickly. The value is that AI can rapidly connect:

user need → product intent → design → requirements → code impact → implementation plan → issues → tests → security → PR → deployment evidence

That chain used to be slow because humans had to manually research, understand, translate, define, then take action across each step. Agentic AI compresses EVERY layer.

The traditional agile release model is being compressed. Work that once required multi-week discovery, design, requirements, implementation, testing, remediation, and deployment cycles can now be accelerated through agentic-AI-assisted software delivery.

This session explores the mindset shift required to move from sequential agile execution to an agentic-AI release factory, where user research, UI/UX design, PRD development, implementation planning, issue creation, parallel development, test evolution, security scanning, and draft pull requests become connected outputs of one intelligent workflow.

The presentation will show how discrepancy reports, feature requests, trouble tickets, user behavior, codebase analysis, schemas, functions, and emerging DX/CX trends can now feed directly into PRDs, implementation plans, EPICs, issues, tests, and deployment-ready pull requests. The result is not reckless speed. It is safe change velocity: faster movement through the SDLC with more context, better traceability, stronger testing, and automated governance.

Attendees will learn:

1. How agentic AI is compressing the traditional SDLC from multi-week agile release cycles into rapid, evidence-driven delivery loops measured in days.
2. How MCP tools enable deeper system awareness by allowing AI agents to interact with files, codebases, schemas, functions, browsers, development tools, and enterprise systems in a more structured way.
3. How Playwright simulations transform testing by automating end-to-end user journeys, UI click-throughs, regression paths, and browser-based validation that previously required extensive manual QA effort.
4. How automated code, OS, container, and dependency scanning strengthen release confidence by identifying vulnerabilities, outdated packages, configuration risks, and remediation paths earlier in the lifecycle.
5. How package remediation can become part of the delivery workflow instead of a separate maintenance backlog, allowing teams to detect, prioritize, and resolve dependency risks continuously.
6. How automated draft PR generation changes the role of the pull request from a simple code review artifact into a release evidence package containing implementation context, test results, security findings, deployment checks, and reviewer guidance.
7. How teams can move faster without becoming reckless by pairing AI acceleration with traceability, human review gates, automated testing, and measurable release readiness criteria.
8. How the mindset of software delivery must shift from “How much can we complete in a sprint?” to “How quickly can we safely move from validated need to secure, tested, deployment-ready change?”