Agentic AI Lifecycle | Plan to Pull Request | GH-600

What happens after an AI agent makes a plan? Can we actually keep that plan visible and reviewable? In this video, we explore the Agentic AI lifecycle and then take it one step further — into a practical GitHub workflow. https://github.com/gauravkhuraana/GH600/blob/main/.github/copilot-instructions.md https://github.com/gauravkhuraana/GH600/blob/main/.github/pull_request_template.md Instead of treating an AI agent's plan as something temporary, we'll look at how a GitHub Pull Request and pull_request_template.md can help capture and review the plan behind AI-generated changes. You'll see how a PR template can capture things like: - Goal / Plan - Scope - Success Criteria - Rollback Plan - Agent Context - Validation / Results - Human Review The bigger idea is simple: Don't just let an AI agent make changes. Make the plan behind those changes visible and reviewable. We'll also use practical examples and the Google Maps rerouting analogy to understand why agentic systems may need to continuously evaluate results and adjust their approach. In this video What happens during an Agentic AI lifecycle Why an agent's plan can change How agents evaluate whether they've achieved the expected result How to keep AI agents bounded Using GitHub Pull Requests for AI-generated changes Creating a .github/pull_request_template.md Capturing the plan, scope, success criteria and rollback considerations Why human review still matters This video is part of my GH-600 / GitHub Copilot Agentic AI learning series, focused on understanding the concepts behind Agentic AI and applying them to real software-development workflows. 🌐 gauravkhurana.com #AgenticAI #GitHubCopilot #GH600 #AIAgents #GitHub #AIEngineering #SoftwareDevelopment #GenerativeAI