Building Autonomous AI Agents
Following our journey of creating AI systems that think, learn, and take action
Technical deep-dive into AI agent evaluation: multi-dimensional scoring frameworks, LLM-as-a-judge testing, regression testing strategies, production monitoring metrics, and best practices for ensuring agent quality in real-world deployments.
Discover how RAG (Retrieval-Augmented Generation) powers smart agents: personalized research assistants, document search, meeting summaries, personal knowledge bases, health coaches, and shopping assistants - no technical background required.
Technical deep-dive into RAG architectures: hybrid query engines, multi-source knowledge orchestration, hierarchical context management, query decomposition, and production optimization patterns for scalable agents.
Practical examples of AI agents enhancing daily life: smart email management, meeting coordination, travel planning, learning companions, budget tracking, meal planning, health monitoring, and family logistics - all without coding.
Technical deep-dive into securing AI agents for production: input sanitization, access control, sandboxed execution, circuit breakers, checkpoint recovery, audit logging, and compliance frameworks for safe autonomous agents.
Discover how AI agents can transform everyday tasks for non-technical users: email management, research assistance, budgeting, learning, family organization, and personal productivity without coding.
Autonomous AI agents represent a paradigm shift in how we interact with technology. Unlike traditional software that requires step-by-step instructions, AI agents can reason about tasks, make decisions, and take action—potentially transforming everything from personal productivity to enterprise operations.
Follow along as we build systems that can help us work smarter, not harder. We'll share our journey, experiments, and insights from the cutting edge of AI development.