Aug 28, 2026

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

Figma's internal AI agent adoption journey faces challenges like reduced developer agency, skepticism from senior engineers, and communication inefficiency. Solutions include investing in verification, using a testing pyramid for agent review, prioritizing detailed planning over prompting, engaging skeptics to build AI safety roadmaps, and implementing attention-aware communication by clearly marking AI-generated content.

Jul 20, 2026

In the Land of AI Agents, the Verifiers Are King — Tariq Shaukat, Sonar

This talk addresses the critical challenge of verification in AI agent development, moving beyond generation to ensure correctness. It highlights the problem of "AI slop" and the "productivity paradox" of AI coding agents, where initial velocity gains are offset by increased technical debt and quality issues. The speaker introduces the AC/DC (Agent-Centric Development Cycle) framework comprising three stages: Guide (providing context and constraints), Verify (zero-trust, multi-layered verification using both algorithmic and agentic methods), and Solve (active code maintenance to control technical debt). This systems-level approach, integrating verification across agentic, CI, and code maintenance loops, significantly reduces issues and transforms AI into a reliable enterprise asset.

Jul 17, 2026

The Great Loops Debate — Dex Horthy, Geoff Huntley, Ian Livingstone, Greg Pstrucha, @insecure-agents

An Oxford-style debate exploring the gap between the hype and practical reality of "loops" in AI/ML development. Experts discuss their history, optimal anatomy, future role in software factories, and challenges like security, economic viability, and the imperative for strong engineering discipline.

Jul 13, 2026

What is an AI Code Generator? LLM Coding, Productivity, & Risk

AI code generators leverage LLMs to translate natural language into code, significantly boosting developer productivity and job satisfaction by automating boilerplate and accelerating learning. However, they introduce risks like subtle security vulnerabilities and require rigorous human review. Evaluating tools hinges on trust, demanding features like data provenance, governance, secure deployment, and curated training data for enterprise adoption.

Jul 06, 2026

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

This video explores AI code refactoring, differentiating between inline and autonomous agentic approaches. It highlights how AI can leverage pattern recognition for tasks like improving readability or reducing duplication, thereby addressing technical debt. A key focus is on the safety guardrails, detailing a multi-step, human-in-the-loop process involving planning, searching, reporting, human approval, patching, and verification through testing, ensuring AI-driven changes are safe for production and can integrate into CI/CD pipelines.

Jun 29, 2026

What Is AI Code Review? Fixing Slow PRs & Broken Workflows with AI

Anna Gutowska explains how AI code review enhances software development by addressing the slowness and inconsistency of traditional methods. The video delves into the benefits of AI in accelerating reviews, improving code quality, fostering developer learning, and reducing technical debt. It covers the underlying technologies like static/dynamic analysis and LLMs, discusses critical considerations such as over-reliance and context, and provides best practices for integrating AI while emphasizing the indispensable role of human oversight.