Agents Without Code: Skills, YAML, and Filesystems Replaced Python — Philipp Schmid, Google DeepMind
Summary
The presentation details the evolution of LLM agents, demonstrating a shift from complex, brittle Python code loops to declarative, file-based definitions using system instructions and skills. The speaker shows that modern agent architectures, such as the Gemini API's anti-gravity agent, utilize a hosted sandbox and network proxy to manage state and credentials securely. This allows agents to operate using general-purpose tools (like GitHub CLI or Google Search) defined in files (e.g., `AGENTS.md`), drastically reducing the need for thousands of lines of custom orchestration code.
Key takeaways
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The Agent Evolution: Code to Files
Agent development is moving away from writing explicit Python loops, JSON schemas, and tool routing logic. The core functionality is now expressed in files (Markdown/Skills) that define instructions, rules, and capabilities, allowing the model to use general tools.
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Server-Side State Management
14:02
The new architecture handles complex tasks by moving loops, tool routing, session state, and context compaction to the server side, requiring only a single API call with new inputs.
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Security and Isolation
12:32
A hosted sandbox and network proxy ensure that the agent never sees the actual credentials, injecting tokens only when outbound requests are made, and allowing domain restriction for enhanced security.
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Focus on Domain Logic
16:40
The primary work for developers is now defining the domain instructions, rules, and evaluation criteria (the 'what'), rather than writing the infrastructure code (the 'how').
Technical details
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Interactions API (Gemini API)
120s
This unified interface supports running models and agents, featuring server-side state management and background execution. It replaced the traditional turn-based conversation history with a 'steps' timeline (user input, reasoning, function call, function result).
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Agent Architecture Shift
360s
The process moved from requiring explicit Python loops, defining JSON schemas, and writing tool implementations to using files (e.g., `AGENTS.md`) to provide system instructions and capabilities (Skills).
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Sandbox Environment
752s
The anti-gravity agent runs within a hosted, isolated cloud sandbox, enabling it to execute bash commands and save files, while the network proxy secures credentials by injecting tokens on outbound requests.
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Agent Definition
880s
Agents can be defined using a custom ID, system instruction, base agent, and environment, allowing existing code and configurations to be reused while leveraging the new API structure.
Mentioned resources
- Interactions API
- Gemini API
- GitHub CLI
- AGENTS.md
Channel & topics
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