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
Master retry strategies, compensation logic, idempotency keys, and dead letter handling to build resilient agent workflows that recover gracefully from partial failures.
Implement production-grade retry logic for LLM API calls using exponential backoff, jitter, and the Tenacity library. Learn when to retry, when to stop, and how to avoid the thundering herd problem.
Build an AI agent that handles returns and refunds by enforcing company policies, managing approval workflows, processing partial refunds, and handling exceptions with proper escalation for edge cases.
Learn how to implement structured message types in chat agents including action buttons, quick reply chips, media cards, carousels, and interactive form elements that go far beyond plain text responses.
Trace the evolution of AI from simple rule-based chatbots to fully autonomous digital workers. Learn the capability milestones, industry adoption patterns, and what the trajectory means for businesses and developers.
Humanoid robots are moving from lab demos to real factory deployments. Explore the engineering breakthroughs in dexterity, balance, and AI that make this possible.
Learn how assigning specific roles and expertise to LLMs dramatically improves response quality. Covers proven persona patterns, role combinations, and techniques to minimize hallucination in role-based prompts.
Learn how to implement the Router pattern for AI agents, using classification-based routing with confidence thresholds and fallback routes to direct requests to specialized handlers.
Learn how to create effective operational runbooks for AI agent systems, covering runbook design principles, step-by-step troubleshooting procedures, automation opportunities, and knowledge transfer practices.
