AI Agents

Explore practical applications of AI agents for team collaboration, personal automation, and complex problem-solving. Learn about managing costs, selecting models, and implementing critical security measures for effective AI integration.

Learn how to safely deploy AI agents that perform real actions like refunds and database writes, focusing on deterministic control layers and separating LLM intent from critical execution.

Discover why outdated or incorrect `Agent.md` files can be more harmful than having none, and learn best practices for creating effective, minimal, and evolving agent documentation.

Discover the critical factors influencing how AI agents select and use tools, from crafting precise descriptions and schemas to managing agent context effectively. Learn strategies for optimizing your tools for the autonomous agent economy.

Explore the emerging field of AI agent orchestration for code development, dissecting the claims of rapid productivity against real-world challenges, costs, and integration hurdles. Learn about effective strategies and practical insights from developers navigating this new frontier.

Struggling to make AI models like Codex adhere to your coding styleguide? Discover practical strategies, from leveraging automated formatters and pre-commit hooks to re-evaluating your approach to code aesthetics, to ensure your AI-generated code meets quality standards.

Explore the current capabilities of local AI models on consumer hardware, their performance gap compared to SOTA, and innovative strategies for their future development.

Discover the challenges of over-permissioning AI agents in cloud and SaaS environments and learn practical strategies for implementing fine-grained access controls. Explore tips for selecting secure platforms, leveraging Workload Identity Federation, and using proxy layers.

Explore the legal ramifications when AI agents accept software licenses on your behalf. Understand agency law, liability boundaries, and practical strategies to manage risks from automated actions.

Explore cutting-edge strategies for securing sensitive data when AI agents operate on local machines. Learn about proxy-based access, runtime secret injection, and context scrubbing techniques.