#Ai Agents

Exploring the exponential growth of AI agent capabilities in software engineering, from simple tab completion to full autonomous development, and what this rapid evolution means for the future of programming.

Discover how Pydantic AI enables developers to build reliable, type-safe AI applications with structured data validation and agent-based architectures.

Master Claude Code's revolutionary Custom Subagents feature to create specialized AI assistants for performance analysis, code optimization, and automated workflows in your development projects.

Comprehensive guide to implementing secure identity management for AI agents, covering authentication patterns, authorization frameworks, and emerging standards for enterprise-grade agentic systems.

Learn how to build reliable AI agents using reinforcement learning through a comprehensive case study that achieved 94% accuracy while reducing costs by 90%.

Learn how to master context engineering for AI agents through four key strategies: writing, selecting, compressing, and isolating context to optimize agent performance and manage token limitations effectively.

Learn how Zapier transforms AI agent failures into features using data-driven evaluation frameworks, feedback loops, and strategic testing approaches.

Explore the common deployment headaches developers face with AI agents and discover infrastructure solutions that enable stateful, long-running agent deployments beyond traditional serverless limitations.

Learn how Strands, an open-source SDK from AWS, simplifies AI agent development to just models and tools, eliminating complex scaffolding and enabling rapid deployment.

Learn the architectural patterns and engineering principles that transform experimental AI agents into reliable, production-grade software systems.

Learn the essential principles for building production-ready AI agents that move beyond simple prompt-and-loop patterns to create truly reliable LLM-powered software.