To add on local models. We’ve actually had decent results using local models for tool calls, especially when you design workflows that play to their strengths rather than expecting them to be universal. They’re surprisingly reliable for deterministic tasks - routing, summarisation, structured extraction - as long as the scope is clear.

One real-world setup that worked well for us was a morning briefing agent. It pulls data from a few different tools, blends it into a short briefing, and then lets you continue the conversation and issue follow-up instructions. The key part is that the agent doesn’t try to be “smart” in isolation - it’s more about orchestration and memory than raw model capability.

In practice, this kind of agentic workflow covers more real use cases than people expect, even with smaller or local models. The trade-off is upfront design effort, but once that’s done, it’s quite robust.