Agents can generate code, but understanding system context remains the hard part. Teams succeeding with AI maturity have a context layer that gives agents exactly what they need, avoiding wasted time and tokens in correction loops. This week's system design refresher explores the top nine places to use Jev, Type Safe AI's first System One Model, which is 100x faster and cheaper than frontier LLMs. Unlike large models skipped for cost, Jev opens new use cases.

Key applications include model routing, where Jev directs prompts to proper LLMs, and guardrails that detect security risks before passing prompts along. Other uses involve gating tool calls, triaging inboxes, reranking passages, and performing LLM evaluations. The model excels at bulk labeling and real-time decisions, such as trading, while a confidence gate ensures proper action based on results.

The core principle uses LLMs for generation and Jev for the surrounding decisions. Additionally, the article distinguishes between MCP and function calling, noting both allow LLM tool access but differ in implementation location. Local function calling runs on the user's machine, while MCP functions can reside on remote servers.

The piece also defines AI agents and Agentic AI, explaining how orchestration layers coordinate multiple agents toward shared objectives through planning and feedback loops.