Why Software Engineering Is Becoming: Plan and Review

Bookmark
Rate this content

Code generation scales. Planning and review don't, limiting how fast AI-native teams can ship. This talk traces the evolution of developer tooling to the current explosion of coding agents, and makes the case for why the biggest opportunity now is helping engineers plan and review faster.

This talk has been presented at AI Coding Summit 2026, check out the latest edition of this Tech Conference.

FAQ

AI coding agents can significantly reduce the time spent writing code, allowing engineers to focus more on planning and reviewing, which can lead to shipping more work efficiently.

The key aspects to optimize are planning and reviewing. Spending more time planning can save time in reviewing, which is a crucial part of the workflow.

Spending more time on planning can reduce the need for extensive reviewing, as a well-structured plan can lead to more accurate AI-generated code changes.

One major mistake is having a complicated dev setup, which can make reviewing changes time-consuming. Simplifying the dev environment can facilitate quicker reviews.

AI can be used to review changes before a human review, identifying necessary changes and prioritizing them, which saves time and improves efficiency.

Working on multiple tasks in parallel allows continuous productivity, as coding agents can work on different tasks simultaneously, keeping the engineer engaged.

Automating repetitive tasks requested by AI can enhance productivity by reducing manual setup and execution time, allowing engineers to focus on more complex tasks.

You can learn more by following the co-founder Louis on Twitter at @tokengobbler or visiting vibekanban.com for guides and tips on working with coding agents.

Vibe Kanban is a tool that helps software engineers work with coding agents to optimize their workflow, increase productivity, and accomplish more.

It's important to have a system of record, such as Vibe Kanban, Notion, or Google Docs, to manage tasks efficiently and keep track of progress.

Louis Knight-Webb
Louis Knight-Webb
18 min
26 Feb, 2026

Comments

Sign in or register to post your comment.
Video Summary and Transcription
Louis from Vibe Kanban discusses optimizing workflows for software engineers with coding agents, focusing on planning and review. Leveraging AI tools like GitHub Copilot and ChatGPT can enhance productivity by reallocating time effectively. Reflecting on time saved by AI, the focus is on optimizing planning and reviewing to boost productivity. Emphasizing detailed planning processes and effective use of AI coding agents to ensure successful outcomes. Simplifying reviewing processes to save time and enhance productivity through efficient code review. Leveraging Codex and Claude coding agents for efficient code review to optimize time and workflow. Minimizing time spent on code review by providing feedback to coding agents within the editor, working on multiple tasks in parallel, and automating tasks requested by AI for enhanced productivity.
Video transcription and chapters available for users with access.

Check out more articles and videos

We constantly think of articles and videos that might spark Git people interest / skill us up or help building a stellar career

Design to Code Using a Custom Design System with AI
React Summit US 2025React Summit US 2025
19 min
Design to Code Using a Custom Design System with AI
Chaitanya, Principal Engineer at Atlassian, discusses the design system at Razorpay, the impact of AI on UI development, and the integration of AI with design systems for enhanced productivity. Detailed prompts for AI to build UI components can be cumbersome. Imagine a seamless process where AI interprets Figma designs to create UI. Leveraging design expertise and focusing on business logic, not writing detailed AI prompts. Blade's MCP server facilitates the magic of transforming Figma designs into UI code by collaborating with Figma and OpenAI.
Powering Cody Coding Assistant Using LLMs
C3 Dev Festival 2024C3 Dev Festival 2024
29 min
Powering Cody Coding Assistant Using LLMs
This Talk explores the world of coding assistants powered by language models (LLMs) and their use cases in software development. It delves into challenges such as understanding big code and developing models for context in LLMs. The importance of ranking and code context is discussed, along with the use of weak supervision signals and fine-tuning models for code completion. The Talk also touches on the evaluation of models and the future trends in code AI, including automation and the role of tasks, programming languages, and code context.