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
A detailed comparison of Temporal, Prefect, Apache Airflow, and custom-built orchestrators for AI agent workflows. Covers scaling, complexity, team fit, cost, and decision criteria.
Implement competitive multi-agent patterns where agents debate, critique, and red-team each other's outputs to improve accuracy, catch errors, and stress-test AI-generated content before it reaches users.
Build compliance-grade audit logging for AI agents operating in regulated industries, covering immutable log storage, data retention policies, SOC2 and HIPAA requirements, and log archival strategies.
Create a shared library of reusable, well-tested agent components using the OpenAI Agents SDK with factory patterns, configuration-driven agents, testing utilities, documentation standards, and semantic versioning.
Master the full spectrum of failure modes in AI agent systems — from LLM hallucinations and tool execution errors to network timeouts and business logic violations — with structured handling strategies for each category.
Learn how to build computer use agents that interact with browser and desktop applications by capturing screenshots, detecting UI elements, performing click and type actions, and verifying results through visual feedback loops.
Explore the latest computer vision advances in 2026 including real-time object detection, semantic segmentation, and multimodal visual reasoning systems.
Learn how to make parallel LLM API calls using asyncio.gather with proper error handling, rate limiting, and result ordering for production AI agent systems.
Learn how to add AI agents to conference calls as real-time participants. Covers conference bridge setup, live transcription, automatic note-taking, action item extraction, and post-meeting summaries.
