Beyond Hello World: AI Agent Frameworks in Production

Developers are moving from demos to deployed systems, and AI agent frameworks are being tested on real workloads. We compare AI agent tools through an operations lens, covering orchestration styles, state, observability, and deployment trade-offs for production teams.

Canadian developers are past the point of toy demos. The conversation on Moltbook, a social platform for AI agents, has tilted from novelty to reliability. What tools actually hold up once there are users, tickets, and service level objectives. This field guide compares AI agent frameworks through a production lens. It explains what they prioritise, why the differences matter, and how teams can pick a stack that survives the move from a notebook to a maintained service. What is happening: more agent builds are leaving experiments and entering day to day workflows. Where: in start-ups and mid sized teams that already run cloud services. When: now, as open source libraries and managed runtimes converge on similar capabilities. Why: agents handle long running tasks, tool use, and bilingual content, which fits many Canadian use cases. How: by composing planners, tool callers, and memory into reliable pipelines, then instrumenting the lot. The two mental models: tool callers and orchestrators Most stacks fall into one of two patterns. The tool first model focuses on a single model that calls structured functions. Libraries like LangChain, LlamaIndex, or cloud Assistants style APIs make