A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. Engineers are picking frameworks based on hype, not fit. Includes controls, pitfalls, and a phased implementation path. A no-fluff comparison of the three dominant agent frameworks — what they're good at, where they break, and how to pick one for production workloads. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour. Defined quality and security acceptance criteria. Practical ...
Practical AI engineering guidance on agents, MCP, frameworks, security, ethics, and AI in insurance — implementation-first, production-ready.