As developers increasingly integrate AI coding agents into their workflows, a new challenge has emerged: ensuring these agents can reliably discover and use a product's specific tools, such as SDKs, CLIs, or documentation. Gauge addresses this by providing a framework to measure and improve 'Agent Experience.' The tool identifies specific friction points where agents might fail, such as when an agent reaches for an outdated API pattern, gets blocked by an interactive command, or fails to recognize a useful 'Skill' even when it is available.
The logo represents the Gauge framework which identifies friction points for AI coding agents.
To solve these issues, Gauge implements a 'hillclimbing' loop: a cycle of making a change, measuring its impact, and repeating the process. The tool runs real agents—including support for Claude Code, Codex, Pi, and OpenCode—against a product in a controlled environment. In local mode, Gauge spins up Docker containers to provide a fresh sandbox where the agent can attempt a defined task. The tool then captures and normalizes the agent's actions into consistent transcripts. Developers can then use these transcripts to see exactly where the agent stalled or made a confident but incorrect claim about a limitation.
The iteration process is designed to be collaborative between the developer and their coding agent. After identifying friction, the coding agent can analyze the evidence and propose specific fixes to the product's documentation, help text, or code. Gauge allows developers to validate these proposed fixes before they are published by rerunning the same tasks against the new artifacts. While local execution offers privacy and avoids per-run costs, it requires the user to manage Docker and agent credentials. Conversely, the hosted version provides managed infrastructure, supports authenticated APIs and Model Context Protocols (MCPs), and offers shared workspaces for teams, though it requires a Gauge account and the use of credits.
One practical limitation of the current tool is the distinction between local and hosted capabilities. For example, while local mode supports Skills and npm packages, hosted mode is required for authenticated APIs and certain custom user personas. Additionally, while the tool provides a structured way to compare 'before' and 'after' batches of agent runs, the specific performance overhead of running multiple concurrent Docker containers locally remains a variable for developers depending on their hardware capabilities. Ultimately, Gauge shifts the focus from manual documentation review to empirical, agent-led testing to ensure software remains accessible to the next generation of automated users.