Pairing context compression with runtime guardrails

[ishita-0301]

I've been experimenting with ways to make long running AI agents both cheaper and more reliable.

Headroom caught my attention because it tackles one side of the problem really well by compressing context before it reaches the model. Keeping prompts lean can save a huge number of tokens without sacrificing too much useful information.

On the other side, I've been looking at FailproofAI, an open source project that focuses on what happens during execution. It helps detect issues like agents getting stuck in loops or repeatedly following failing paths instead of making forward progress.

Repository: https://github.com/FailproofAI/failproofai

To me, these approaches complement each other nicely. One reduces unnecessary context and cost while the other improves runtime reliability. I'm curious if anyone else is combining context optimization with execution guardrails in production agent systems.