2027 is the standard for measuring how well digital products work for AI agents. Our platform continuously scores, benchmarks, and certifies agent experience across the software ecosystem.
By 2027, autonomous coders will write the majority of all software. Code will become the new machine code — produced at a scale and speed no human can review. The systems we depend on will be built, maintained, and evolved by agents.
2027 exists to ensure this transition benefits everyone. We are building the measurement infrastructure — the scoring systems, benchmarks, and certification standards — that the software industry needs to navigate the shift from human-first to agent-first products.
Just as SOC 2 became the universal trust standard for security compliance, 2027 is becoming the universal standard for agent compatibility. A future where every devtool, API, and SaaS product displays an agent-readiness score — and where that score directly influences adoption.
We envision a world where products compete on agent experience the same way they compete on developer experience today. 2027 provides the benchmark that defines what "agent-ready" means.
AI agents already consume more documentation and APIs than humans. They don't read your carefully crafted UI — they parse, execute, and move on. If an agent can't navigate your product, it won't use it. No one was measuring this.
Claude Code, Codex, Cursor — these tools are shifting from copilots to autonomous agents. Agent traffic on docs sites is growing exponentially. Companies can see it but can't measure impact. There was no standard for agent readiness — until now.
Real AI agents continuously test products, scores update automatically, and companies get the data they need to become agent-ready.
Public leaderboard ranking devtools by agent-readiness. Free, open, and transparent. The benchmark that defines the category.
Automated evaluation platform with agent traces, waterfall timelines, and actionable recommendations. See exactly where agents get stuck and how to fix it.
Open-source npm package to detect and measure AI agent traffic on your docs. Two lines of code. Server-side only. No cookies. No PII.
An AI coding agent receives a single prompt — "set up [tool]" — and the platform measures everything that happens.
| Metric | What it measures | Weight |
|---|---|---|
| Setup Friction | Times the agent paused and needed human input | 25% |
| Setup Speed | Wall-clock time from prompt to working hello-world | 20% |
| Efficiency | Number of tool calls — fewer means better docs | 20% |
| Error Recovery | Errors hit before success — how forgiving is the setup | 15% |
| Doc Quality | Agent-friendliness: markdown, code examples, API completeness | 20% |
Agent Arena is public and open. The ecosystem improves when everyone can see how products perform for AI agents. Scores, methodology, and criteria are never behind closed doors.
Every evaluation follows a reproducible methodology — real agents, real prompts, real metrics. No synthetic benchmarks, no guesswork, no pay-to-play rankings.
The platform evaluates from the agent's perspective. If an AI can't navigate it autonomously, it's a friction point worth fixing — regardless of how it looks to humans.
Get scored, get certified, stay agent-ready.