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
Implement comprehensive timeout strategies for AI agent pipelines including cascading timeouts, deadline propagation, and proper cleanup of abandoned requests to prevent resource leaks.
Master timezone detection, locale-aware date formatting, and cross-timezone scheduling in AI agents to deliver accurate, localized time information to users worldwide.
Build a token budget management system with per-user quotas, per-request limits, enforcement middleware, and graceful degradation. Prevent cost overruns while maintaining service quality for your AI agents.
Learn techniques for detecting and repairing common LLM output problems including truncated responses, malformed JSON, encoding artifacts, and broken code blocks through robust post-processing pipelines.
Master prompt compression, context pruning, conversation summarization, and selective history techniques to cut LLM costs and latency while preserving response quality in your AI agents.
Learn how to track token consumption across AI agents, attribute costs to specific features and users, identify usage trends, and implement optimization strategies that reduce LLM spend without sacrificing quality.
Implement a robust human-in-the-loop approval system for sensitive agent actions using the OpenAI Agents SDK with approval gates, notification channels, configurable timeouts, and auto-approve rules.
Learn how to build robust permission models for AI agent tool access, including policy engines, dynamic permissions, role-based access control, and comprehensive audit logging for every tool invocation.
Master the art of formatting tool results so LLMs can effectively parse and reason about them. Covers string formatting strategies, truncation, structured vs unstructured results, error messages, and token-efficient output design.
