Both- depending on the work. CLI for anything local and deterministic: code tools, builds, git, file ops. The agent already knows how to exec commands and parse output, and when something breaks you get stderr and an exit code. MCP for discovery and remote services where the agent needs to figure out what's available.

They solve different problems, but imo MCP has a speed overhead problem that cli tools don’t usually have - so my preference is CLI.

I think it’s kinda one of those YMMV metrics. I work for a large company around 80-100k employees and they are ranked on this list. The business unit I’m in is amazing and I truly feel that it’s a great place to work. But ask someone in a dying business unit how they feel and they’ll say they’re overworked and understaffed.

I have found this to be true too and I thought I was the only one. Everyone is praising 4.6 and while it’s great at agentic and tool use, it does not follow instructions as cleanly as 4.5 - I also feel like 4.5 was just way more efficient too

I think that's because not everyone does the same job within the same stack and constraints. I'm yet to find an LLM that writes the kind of C++ I dabble with without having to manually tweak it myself (or that truly understands our codebase). Conversely, I find that LLMs are now excellent at python and orchestration tasks for instance. It's very situational

100% - you are very right. 4.6 is amazing for orchestration. I even built some tools around agent to agent contracting.

I use 4.6 as the brain and then handoff to a more rigid llm like GPT 5.2 or Opus 4.5

I think the 'AI productivity gap' is mostly a state management problem. Even with great models, you burn so much time just manually syncing context between different agents or chat sessions.

Until the handoff tax is lower than the cost of just doing it yourself, the ROI isn't going to be there for most engineering workflows.