Speed versus reliability in personal AI agent systems
A personal must turn a request into smaller actions, then choose and connect the right executors or agents to produce an answer. One request can include work across files, email, databases, calendars, and other areas, so the routing problem becomes difficult.
When there are hundreds of possible executors and the set can change, the system needs ways to understand the request, narrow the choices, and connect the right pieces in order. The design mixes rules with statistical LLM judgment.
A fully system would be faster and more predictable, but it would force people to speak in a stiff, limited way. A more LLM-driven system can handle better, but it can also be uncertain, and speed tools such as cache and MTP may add new risk if they change how decisions are made.
Key points
- A personal needs to split requests into actions and connect them to executors or agents.
- Requests can span several domains, such as files, email, databases, and calendars.
- Large numbers of possible executors make intent handling and candidate filtering harder.
- rules are faster and more predictable, but they can make the assistant rigid.
- LLM-based judgment is more flexible, but it brings and cost .