We're building Google for AI Agents

Source: Dev.to

The Need for a Google‑like Search for AI Agents

In the early days of the web, information existed but finding it was chaotic. Yahoo built a directory where people browsed categories—it worked, until it didn’t. As content exploded, the problem shifted from merely locating pages to identifying which ones were worth your time.

Google solved that with PageRank, using the structure of links as a signal of trust. Quality rose to the top.

Now, fast‑forward to AI agents and APIs. A similar story is unfolding at a different layer.

The Current State

• Thousands of x402‑enabled APIs exist—APIs that AI agents can call autonomously, pay for instantly, and use without human intervention.
• Agents can’t find them, evaluate them, or choose between them.
• Developers therefore hard‑code an endpoint, hope it works, and move on.

This is the “Yahoo era” of AI‑agent APIs.

Introducing Decixa

Decixa provides both the directory layer (collecting every x402 endpoint) and the quality layer (probing each one, tracking uptime, and ranking by reliability).

How It Works

1. Discovery – We gathered 20,000+ endpoints from across the x402 ecosystem.
2. Verification – Each endpoint is probed to see if it returns a valid 402 payment response. Only 3,800+ pass this test.
3. Indexing – Verified, live APIs are indexed and exposed to agents.
4. Ranking – Results are ordered by reliability, cost, latency, and a trust score.

The gap between the 20k+ collected endpoints and the 3.8k+ verified ones highlights that most endpoints in the wild don’t implement 402 correctly. Agents shouldn’t see them.

Using Decixa

CLI

npx decixa-mcp

API Call

curl -X POST https://api.decixa.ai/api/agent/resolve \
  -H "Content-Type: application/json" \
  -d '{"intent": "extract social media posts by keyword"}'

The response includes:

• Capability
• Cost
• Latency
• Trust score

All the information an agent needs to make a decision without human input.

Why It Matters Now

The x402 ecosystem is still young, but the number of endpoints will grow rapidly, making the quality problem harder. Building the infrastructure while the ecosystem is small enough to probe completely gives agents reliable, up‑to‑date information.

Call to Action

If you’re building AI agents, we’d love your feedback.

• Try it: npx decixa-mcp
• Explore:
• Follow: @decixa_ai