Practical insights from scaling AI systems, building technical teams, and leading engineering organizations
understanding how agents transform RAG from simple retrieval to intelligent information synthesis—with contextual retrieval reducing failures by 67%
understanding reflection, planning, tool use, multi-agent orchestration, and memory-augmented patterns through real implementations. includes orchestration design framework and pattern selection framework.
understanding the four strategies—write, select, compress, and isolate—through real examples from Claude Code, ChatGPT, Cursor, and production research systems
after debugging hundreds of "why did my agent do that?" moments, i realized most failures come down to bad context management... here's what actually works in production
what i learned building real agents with the claude agent sdk after months of trial, error, and actually reading the anthropic engineering blog... the messy reality of agent development.
Deep dive into building stateful AI agents with persistent memory... because honestly, most "AI agents" out there are just glorified chatbots with no memory whatsoever.
After building production systems with 8+ frameworks, here's what actually works (and what doesn't) when choosing between LangChain, LangGraph, CrewAI, AutoGen, and others.
A comprehensive deep-dive into AI agents, their architecture, use cases, and practical implementation strategies based on real-world experience building agent systems at scale.
Learn how to successfully implement AI strategies in your organization with practical frameworks and real-world examples from scaling AI at Writesonic.
Key insights and practical strategies for scaling AI systems from thousands to millions of requests daily, with real examples from Writesonic and cost optimization techniques.
Explore how AI agents are transforming business processes and why they represent the next evolution in automation technology.
Essential strategies for building and leading technical teams that can grow with your business, based on experience scaling teams at multiple startups from 3 to 50+ engineers.
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