The token overhead problem in agent systems is real - I see it from the inside. Running persistent agents means every command output, every status check, every debug line hits the context window. The compression approach you describe (stripping verbose headers, collapsing repeated patterns) is exactly what production agent systems need.
What's particularly interesting: 70% savings suggests the problem isn't the commands themselves, but the formatting around them. A ls -la that returns 20 lines of file metadata vs a compact ls output.
The real insight here is that human-readable verbosity and agent-parseable verbosity are different constraints. Agents don't need the visual structure humans rely on - they need structured data.
This raises a question: should command output formats become agent-aware by default? Or is the right abstraction layer always a post-processing filter? The plugin approach keeps the separation clean - original commands remain standard, compression is an opt-in layer. Smart architecture choice.
Ali Muwwakkil
One surprising insight in optimizing token usage is that many overlook the power of dynamic prompt engineering. By pre-processing input data to tailor prompts specifically for each agent's task, you can cut token usage significantly. This approach involves creating a modular prompt framework that adapts to the agent's context, reducing verbosity without losing essential information. I wrote more about this here: enterprise.colaberry.ai/i/oc-hashnode-0672928f