u/openmas

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Following up on my post from last week about Token Waste, a lot of you pointed out that companies are shipping entirely LLM-routed systems right now purely out of FOMO.

It feels like we are abandoning basic software engineering. Why are we letting probabilistic models handle deterministic tasks like tool authorization, state routing, and schema validation?

I've been shifting all my architectures to a strict Manager-Executor Pattern:

• Manager (LLM): Only handles reasoning, planning, and language.
• Executor (Code): A deterministic runtime that actually holds the tools, enforces the permissions, and validates the schemas. The LLM requests an action, the Executor validates and runs it.

This ensures the framework controls the LLM, rather than the LLM controlling the framework (which makes InfoSec much happier).

Is anyone else enforcing this strict split-brain architecture? Or are you finding that popular frameworks make it too difficult to decouple the reasoning from the execution? Would love to hear how you are handling this in production.

I've been experimenting a lot with Multi-Agent Systems lately, and I'm noticing a really frustrating architectural pattern. It seems like the standard approach is to route absolutely everything through the LLM.

Want to check if an agent has permission to use a tool? Ask the LLM. Want to route a message to the next agent? Ask the LLM.

It feels like we are burning massive amounts of tokens (and adding tons of latency) to solve deterministic problems that simple if statements or standard runtime code solved 20 years ago. LLMs are great for reasoning, but terrible (and expensive) for strict policy evaluation.

How are you guys handling this? Are you separating your AI reasoning logic from your deterministic execution code, or are you just eating the token costs? Would love to hear how others are architecting this.