Announcement · Mar 25, 2026

Former Liblib CTO Starts a New Venture: Building Infrastructure for the Agent Era

Q: Who founded QVeris AI?

QVeris was founded by the former CTO of Liblib AI, one of China's leading AI image generation platforms. After observing the rapid growth of AI agents and the lack of shared tool infrastructure, the founder left to build the capability routing network for the agent era.

Q: What problem is QVeris solving?

AI agents can only use tools they were pre-configured with at build time. QVeris solves the tool discovery and routing problem — letting agents find and call any of 10,000+ verified capabilities at runtime, using natural language, the same way a search engine lets humans find web pages.

Q: Why is 2026 the right time to build AI agent infrastructure?

Enterprise AI agent deployment has crossed 79% adoption. Agents are moving from prototypes to production workflows. The tool fragmentation problem — scattered APIs, no unified discovery layer — is now the primary bottleneck at scale. Infrastructure layers get built early and become defaults.

Q: How is QVeris different from LangChain or other agent frameworks?

LangChain and similar frameworks handle agent orchestration — how agents reason and chain steps together. QVeris operates at a different layer: capability discovery and routing. It works alongside any orchestration framework, not instead of it.

Q: What does action infrastructure for the agent era mean?

It means the shared layer that lets AI agents discover, inspect, route to, and execute real-world capabilities — tools, APIs, live data, and external services — at production scale, with quality signals, audit trails, and consistent execution guarantees.

A founder story about leaving a successful AI image platform to build the action layer that every AI agent will need.

QVeris Founder & CEO

Former CTO · Liblib AI

10,000+

Tools in network

15+

Capability categories

<500ms

Avg tool call latency

Read the Full Story →Explore QVeris →

Why Leave a Successful Company?

The AI image platform was working.

Liblib had built one of the most popular AI image generation platforms in China. The product was growing, the team was strong, and the market was expanding. So why leave?

Because the next decade of AI isn't about generating images. It's about agents that act. And agents that act need infrastructure to find, route, and execute real-world capabilities at scale.

The infrastructure gap was obvious.

Three observations drove the decision:

Observation 1

Agents were multiplying. Every major model lab was shipping agent features. Developers were building agent products. The ecosystem was forming fast.

Observation 2

Tools were fragmented. 10,000+ APIs existed for agents. But they were scattered, unindexed, and incompatible. No unified discovery or routing.

Observation 3

The window was open. Infrastructure layers get built once, early, and become defaults. The window to build the capability routing network for agents was open — but not forever.

The Vision: Action Infrastructure for the Agent Era

Kubernetes + Zapier + Homebrew — for AI tools

🖥

Like Kubernetes

Standardizes how agents discover and route to capabilities — the same way Kubernetes standardized how services discover and route to each other. One control plane for all tool access.

🔌

Like Zapier

Connects agents to thousands of real-world tools and services — without requiring custom integrations for each one. One connection, all capabilities.

📦

Like Homebrew

A trusted, community-verified registry of capabilities — so agents and developers can find and install exactly what they need. Verified, versioned, ready to call.

But purpose-built for AI agents. Not adapted from something else.

What We're Building

The shared infrastructure layer for the agent ecosystem.

Capability Discovery

A semantic search engine for tools. Agents describe what they need in natural language. QVeris returns ranked matches with quality signals. No API names to memorize. No documentation to hunt down.

Intelligent Routing

When multiple providers offer the same capability, QVeris routes to the best match — based on task requirements, provider availability, cost, latency, and success rate. Not random. Not hardcoded. Optimized.

Sandboxed Execution

Every tool call runs in an isolated sandbox with parameter validation, authentication handling, and consistent JSON output. One protocol for all capabilities. No per-provider parsing. No broken pipelines.

Full Audit Trail

Unique execution IDs, session-level tracing, and full call records for debugging, cost tracking, and compliance — built in from day one. Every call logged. Every result traceable.

Why Now

2023

LLMs go mainstream

GPT-4 launches. Developers start building agent prototypes.

2024

Agent frameworks emerge

LangChain, AutoGPT. Orchestration solved. Tool access fragmented.

2025

Agents enter production

79% of enterprises deploy agents. Fragmentation becomes a bottleneck.

2026

Infrastructure layer builds

The window to become the default routing network is open. QVeris is building it.

Future

Every agent on shared infra

Like DNS for the web. Every agent routes through a shared capability network.

What We've Shipped

QVeris NetworkLIVE

10,000+ verified capabilities across 15+ categories. The core routing infrastructure.

View Providers →

QVeris CLILIVE

Universal API gateway from the terminal. Discover, inspect, and call any tool in natural language.

Install CLI →

MCP ServerLIVE

Tool gateway for IDE agents — Cursor, Claude Code, and all MCP-compatible environments.

Set Up MCP →

QVerisBotLIVE

Production AI assistant with native access to 500+ data providers and 10,000+ APIs.

Try QVerisBot →

Frequently Asked Questions

Who founded QVeris AI?

QVeris was founded by the former CTO of Liblib AI, one of China's leading AI image generation platforms. After observing the rapid growth of AI agents and the lack of shared tool infrastructure, the founder left a successful company to build the capability routing network that every AI agent will need.

What problem is QVeris solving?

AI agents can only use tools they were pre-configured with at build time. QVeris solves the tool discovery and routing problem — letting agents find and call any of 10,000+ verified capabilities at runtime, using natural language, the same way a search engine lets humans find web pages they've never visited.

Why is 2026 the right time to build AI agent infrastructure?

Enterprise AI agent deployment has crossed 79% adoption. Agents are moving from prototypes to production workflows. The tool fragmentation problem — scattered APIs, no unified discovery layer — is now the primary bottleneck at scale. Infrastructure layers get built early and become defaults. The window is open now.

How is QVeris different from LangChain or other agent frameworks?

LangChain and similar frameworks handle agent orchestration — how agents reason and chain steps together. QVeris operates at a different layer: capability discovery and routing. It works alongside any orchestration framework, not instead of it. Orchestration manages the reasoning flow. Routing manages the tool access.

What does "action infrastructure for the agent era" mean?

It means the shared layer that lets AI agents discover, inspect, route to, and execute real-world capabilities — tools, APIs, live data, and external services — at production scale, with quality signals, audit trails, and consistent execution guarantees. The same way DNS, HTTP, and cloud infrastructure became the shared foundation of the web, capability routing becomes the shared foundation of the agent ecosystem.