Agent-Computer Interface
Design tools for LLM agents specifically — not for humans at a keyboard — with context-budget-aware affordances.
Intent & Description
🎯 Intent
Build a tool surface optimized for how agents reason, not how humans type.
📋 Context
You’re shipping a coding or research agent that drives a shell, file system, or web page originally built for humans. The agent burns context budget fast on noisy raw output and lacks the quick visual scan a human uses to orient.
💡 Solution
Design agent-specific tools: a file viewer that shows windowed slices with line numbers, an edit tool that reruns the linter and returns the diff, a shell that returns structured stdout/stderr/exit-code, a search that filters and ranks. Every tool signature and return type is tuned for the agent’s context budget and reasoning style.
Real-world Use Case
- Off-the-shelf human tools (bash, vim, browsers) overwhelm the agent with noise.
- You can afford to build and maintain a curated, agent-specific tool surface.
- You measure agent performance and want the tool layer to be a tunable variable.
Source
📌 TL;DR
Don’t hand your agent raw bash and vim. Build agent-native tools with windowed output, structured returns, and ranked search — then tune from there.
Advantages
- Measurable accuracy gains over human-CLI tools on the same tasks.
- Each tool’s design is explicit and inspectable — no magic.
Disadvantages
- Two interface surfaces to keep in sync: one for humans, one for the agent.
- ACI design is empirical — expect several iterations before it’s right.