Scaling agentic coding practices across your team
How can coding agents the exact context they need to build a project without repeating yourself on every single prompt? In this video, Annie Wang and Tilde Thurium break down the markdown-based specification files that define how your AI agents should think, design, and execute tasks across your codebase: * *DESIGN.md:* Visual guardrails, UI design systems, and styling tokens. * *AGENTS.md:* Global project guidance, coding standards, and build/test instructions. * *SKILL.md:* Reusable, domain-specific tasks and workflows. Learn how agent specifications differ from standard prompt and context engineering, see benchmark data on token savings and performance, and discover best practices for scoping shared team rules versus personal developer preferences. Chapters: 00:00 - Introduction & why we need agent specifications 01:00 - Specifications vs. prompt & context engineering 02:50 - DESIGN.md: Visual guardrails & design systems 04:56 - AGENTS.md: Project guidance & global behavior 06:12 - Benchmark study: How AGENTS.md impacts performance 07:28 - SKILL.md: Domain-specific tasks & reusable prompts 08:46 - Best practices: Separation of concerns & consistency 10:17 - Scoping preferences: Project layer vs. user layer 11:43 - Real world examples: Google ADK & DeepMind 12:12 - Conclusion & wrap up Resources & Links Mentioned: * Context engineering deep dive with Smitha → https://g.dev/ai/context-engineering-explained * Skills vs. MCP deep dive → https://g.dev/ai/skills-vs-mcp * Google Labs DESIGN.md specification (GitHub) → https://g.dev/ai/design-skils * Google Agent Development Kit (ADK) → https://g.dev/ai/adk-python * DeepMind Simply repository → https://g.dev/ai/simply-repo * Agentic AI Foundation benchmark study → https://g.dev/ai/benchmark-study 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech Let us know in the comments how you're using AGENTS.md, DESIGN.md, or SKILL.md in your development workflows! Speakers: Tilde Thurium, Annie Wang