Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Learn how to diagnose and fix common streaming problems in AI agents including dropped tokens, connection resets, partial responses, and timeout failures with practical debugging techniques.
Discover how to identify and fix excessive token consumption in AI agents by analyzing prompt bloat, conversation history growth, tool definition overhead, and applying targeted optimization strategies.
Master techniques for diagnosing tool call failures in AI agents, from call logging and parameter inspection to mock execution and replay testing for reliable tool integrations.
A practical guide to diagnosing and fixing voice AI agent issues including audio quality degradation, speech-to-text transcription errors, text-to-speech artifacts, and end-to-end pipeline latency.
Learn practical criteria for decomposing a monolithic AI agent into microservices, including how to identify service boundaries, choose communication patterns, and execute a safe migration without downtime.
Learn how to expose AI agents through production-grade FastAPI REST endpoints with async request handling, Pydantic validation, structured error responses, and streaming support.
Deploy FastAPI AI agent backends to production with optimal Uvicorn and Gunicorn configuration, Docker multi-stage builds, health check endpoints, and graceful shutdown handling for long-running agent requests.
A practical guide to deploying TypeScript AI agents in production. Compare Vercel serverless, Railway containers, and Docker self-hosted strategies. Covers environment configuration, scaling, health checks, monitoring, and cost optimization.
Build consistent and effective AI agent personas with frameworks for voice definition, tone modulation, personality traits, brand alignment, and cultural sensitivity considerations.
