Calling an LLM from a route handler is not agent engineering, the same way calling a database is not backend engineering. Agent Workspace — a mobile agentic coding environment with an LLM hub, sandboxed execution and RAG memory — is where that distinction stopped being a talking point and became a set of real constraints I had to design around.
The model is not the hard part
The hard part is everything around the model: what context it is allowed to see, what it is allowed to execute, and what happens when it is confidently wrong. Sandboxed execution exists because an agent that can run code needs a blast radius, not just a prompt. RAG memory exists because a model with no persistent, retrievable context re-derives the same conclusions every session, which is expensive and occasionally inconsistent.
Agents fail differently than APIs fail
A REST endpoint fails loud — a status code, a stack trace. An agent can fail quiet: a plausible-sounding wrong answer, executed with full confidence. Designing for that means the system needs checkpoints where a human or a deterministic rule can veto an agent’s next step, not just error handling for the calls that throw.
Why this is the headline, not the footnote
Full-stack is table stakes — every serious build still needs a database, an API and a UI, and Agent Workspace has all three. What is scarce is the layer that decides what an autonomous system is allowed to do and how it recovers when it is wrong. That is the actual skill “AI Agent & Automation Engineer” is naming.