@netlify/axis

AXIS — Agent eXperience Index Score

AXIS is a synthetic testing framework for measuring how well systems support AI agent interaction. Think Lighthouse, but instead of scoring user experience, AXIS scores agent experience.

Given a scenario, an agent, and a prompt — AXIS runs the agent, captures a full transcript of the interaction, and produces a graded score across four independent dimensions: Goal Achievement, Environment, Service, and Agent.

Why AXIS

The web has Lighthouse. APIs have contract testing. Performance has k6. But there's no standardized way to answer: "How well does my system work when an AI agent tries to use it?"

As agents become a primary interface for interacting with websites, APIs, and developer platforms, the systems they interact with need to be measured and optimized for that experience — just like we optimize for page load time or accessibility.

Quick Start

npm install @netlify/axis

axis.config.json:

{
  "scenarios": "./scenarios",
  "agents": ["claude-code"]
}

scenarios/hello-world.json:

{
  "name": "Hello World",
  "prompt": "Navigate to https://example.com and describe what you see on the page.",
  "rubric": [
    { "check": "Agent visited the target URL", "weight": 0.5 },
    { "check": "Agent provided a description of the page content", "weight": 0.5 }
  ]
}

axis run

AXIS executes the scenario, scores the result, and writes a report to .axis/reports/.

Documentation

Full documentation lives at https://axis-docs.netlify.app:

• Overview — what AXIS measures and why
• Quick Start — install through your first scored run
• Configuration — axis.config.json, scenarios, MCP servers, skills
• CLI Reference — axis run, axis reports, axis baseline
• Running Tests — execution model, workspace isolation, custom adapters, CI integration
• Scoring Framework — the four dimensions, signals, calibration

Programmatic API

@netlify/axis exports its core functionality for use as a library:

import { run, scoreResults } from "@netlify/axis";

const output = await run({ configPath: "axis.config.json" });
const scored = await scoreResults(output);

console.log(`Average AXIS Result: ${scored.summary.averageAxisScore}`);

The package also exports loadConfig, discoverScenarios, setBaseline, compareBaseline, createAgentAdapter, registerAdapter, and the underlying scoring primitives (buildSparseIndex, categorizeInteraction, normalizeTranscript). See src/index.ts for the full surface.

Roadmap

Delivered: scenario runner, four-dimension scoring pipeline, baselines with regression detection, MCP/skills wiring, custom adapter API, built-in adapters for Claude Code, Codex, and Gemini.

Planned:

• Historical trending — score regression detection over time
• AXIS Badge — embeddable score badge for READMEs
• Configurable judge — separate adapter/model for scoring, independent of the agent under test
• Score thresholds — CI gating with configurable pass/fail thresholds
• Human interruption detection — penalize agent requests for human intervention