Agent Data Engine

Data infrastructure for the next generation of AI agents.

We build the benchmarks, datasets, and long-horizon environments that let frontier agents learn, reason, and act in the real world.

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The evolution of agent data

As AI systems evolve from chatbots to persistent agents, the data infrastructure they need changes fundamentally.

1Chatbot Era

Single-shot prompt → response

Data paradigm

Instruction-tuning pairs and RLHF preference data. Static (prompt, response) examples curated by human annotators.

2Single-Session Agent

One query, one environment, one session

Data paradigm

Agent trajectories in sandboxed environments. RL rollouts, tool-use traces, and reward signals within a single bounded session.

3Persistent Agent

Always-on agents that learn and evolve

Data paradigm

Continuous, multi-day interaction streams with evolving environments, accumulated context, and self-improving agent behavior.

  • Long-horizon tasks spanning hundreds of steps
  • Multi-day, multi-stage workflows
  • Perceiving and adapting to living environment
  • User modeling for proactive action
  • Continuous self-evolution from experience
Evolvent's Focus

What we build

Three pillars, one mission: giving agents the data and environments they need to evolve.

01
Agent Data Engine

Agent-grade datasets and benchmarks

Curated, high-signal training and evaluation data for computer-use, coding, research, and professional-workflow agents. Every example is engineered, verified, and traceable.

02
Proactive Agent

Agents that act before being asked

We research and train proactive agents — systems that anticipate user intent, monitor context, and act on their own initiative rather than waiting for commands.

03
Long-Horizon RL Environments

Realistic environments for multi-step reasoning

Desktop VMs, MCP servers, and end-to-end professional workflows. Train and evaluate agents on tasks that span hundreds of steps and hours of real work.

Shipped Datasets

Production-ready datasets and tools from our data engine

Structured Planning Data

Structured task decomposition data for training planner models with multi-agent coordination capabilities.

ClawMark

A benchmark for openclaw-like coworker agents — 100 tasks across 13 professional domains with multi-day interactions, live environment mutations, and multimodal inputs.

Let's build what's next, together.

Join leading AI teams partnering with Evolvent AI on agent data, benchmarks, and long-horizon environments. Book a 1:1 demo to get started.

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