Deep dives into agentic AI, LLM evaluation, synthetic data generation, model selection, and production AI engineering best practices.
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Discover how agentic AI is transforming insurance claims assessment, fraud detection, and risk underwriting across the US, UK, and European InsurTech markets in 2026.
How to implement end-to-end observability for AI agents using OpenTelemetry traces, LangSmith, and custom instrumentation to debug failures and optimize performance.
Learn how to design and implement multi-agent systems using the Claude API and Agent SDK. Covers architecture patterns, inter-agent communication, task delegation, and real-world production examples.
Explore the architecture, limitations, and practical patterns for running LLM inference and AI workloads on serverless platforms like AWS Lambda and Google Cloud Functions.
Deep dive into the orchestrator-subagent architecture pattern used in Claude Code and the Claude Agent SDK. Learn how task decomposition, delegation, and result synthesis work under the hood.
Complete guide to implementing tool use (function calling) with the Claude API. Covers tool definitions, execution patterns, multi-turn conversations, and production best practices.
A comprehensive guide to understanding, forecasting, and optimizing the costs of running LLM-powered applications in production, with real pricing data and cost reduction strategies.
A practical guide to deploying reasoning and chain-of-thought models in production, covering when extended thinking adds value, cost-performance tradeoffs, and implementation patterns.
