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 define, calculate, and benchmark the core effectiveness metrics for AI agents including resolution rate, containment rate, first-contact resolution, and strategies for systematic improvement.
A practical decision matrix for selecting the right agent framework based on team size, use case complexity, scalability needs, vendor preferences, and production requirements.
Learn how to implement clean agent handoffs using the OpenAI Agents SDK, including handoff triggers, context transfer, conversation continuity, and patterns for preserving user experience across agent boundaries.
A deep dive into the agent loop — the fundamental control flow that powers every AI agent. Learn loop mechanics, termination conditions, maximum iteration strategies, and how to prevent infinite loops.
Explore pricing strategies for AI agents including per-invocation metering, tiered subscriptions, and freemium conversion funnels. Learn how to build billing infrastructure that tracks usage accurately and optimizes revenue.
Learn how to implement negotiation protocols for AI agents including offer-counteroffer patterns, compromise strategies, and deadlock resolution. Build agents that autonomously reach mutually acceptable outcomes.
Learn how to package AI agents into portable, versioned bundles with dependency management, configuration schemas, and reproducible deployments. Build a packaging format that works across teams and environments.
Define and measure Service Level Agreements for AI agent systems with practical guidance on SLA definition, measurement methodology, automated reporting, and penalty handling for production agent deployments.
Learn how to craft system instructions that produce consistent, reliable agent behavior — covering instruction engineering, behavioral boundaries, persona design, and techniques for preventing instruction drift.
