Klavis AI

Klavis AI provides live environments for training and evaluating AI agents, with separate support for coding data and agentic tool-use data. It is aimed at teams building long-horizon agent workflows that need programmatic verification, realistic tool use, and custom environment builds.

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Klavis AI

Overview

Klavis AI provides live environments for training and evaluating AI agents, with a focus on generating coding data and agentic tool-use data for frontier model work. The site positions the product around long-horizon tasks that require code execution, tool use, and verifiable outcomes rather than short prompt-response exchanges.

For coding workflows, Klavis AI emphasizes Dockerized environments, programmatic verification, deterministic tests, and granular rewards for editing, testing, and debugging tasks. For agentic workflows, it describes realistic interactions across live SaaS apps and production MCP servers, with state changes and reward signals designed for complex multi-step behavior.

The contact flow suggests the company can map a requested model behavior to a coding dataset, an agentic tool-use dataset, or a custom environment build. That makes the product relevant for teams that need training or evaluation data aligned to specific workflows instead of generic benchmark tasks.

Core capabilities

Long-horizon coding data

Supports long-horizon coding tasks that include code editing, test writing, debugging, and programmatic checks.

Dockerized environments

Provides Docker-packaged environments for coding workflows, which helps standardize setup across tasks.

Programmatic verification

Uses programmatic verification and deterministic tests rather than manual-only review for coding outputs.

Granular reward design

Offers granular rewards for coding tasks, alongside binary pass/fail signals where appropriate.

Live tool-use environments

Covers realistic agentic tool-use workflows across live SaaS apps and production MCP servers.

Stateful, verifiable workflows

Includes logically consistent state, noisy inputs, and verifiable rewards for tool-use data generation and evaluation.

Common use cases

Coding agent training
Build training sets for coding agents that need to edit code, write tests, and debug across longer task sequences.
Agentic tool-use evaluation
Generate evaluation environments for agents that must operate inside SaaS applications or MCP-based tool workflows.
Custom environment builds
Create custom datasets or environments when a team needs to model a specific behavior, private workflow, or domain-specific task.
Realistic post-training data
Replace demo-style or toy task loops with environments that include state changes, noisy inputs, and verifiable rewards.

Pros and Cons

Pros

Covers both coding and agentic tool-use data in one product.
Focuses on long-horizon workflows instead of short synthetic examples.
Uses programmatic verification and deterministic tests for coding tasks.
Supports realistic, stateful workflows across live SaaS apps and MCP servers.
Offers a custom-dataset path for specific model behaviors or private workflows.

Cons

The captured pricing page does not expose pricing or plan details.
The site text gives limited implementation specifics, so integration and onboarding details are unclear from the provided evidence.

FAQ

What does Klavis AI provide?

Klavis AI is presented as a platform for building live environments used to train AI agents. The site groups its offering into coding data and agentic tool-use data.

What kinds of data does it support?

The site says it supports both coding data and agentic tool-use data. Coding data focuses on long-horizon coding tasks, Docker environments, tests, and debugging loops; agentic tool-use data focuses on realistic SaaS and MCP workflows with state changes.

Can it support custom training or evaluation needs?

The contact page says teams can share the model behavior they want to train or evaluate, and Klavis AI will map it to coding data, agentic tool-use data, or a custom environment build.

Is pricing published on the site?

The source does not show pricing, plan tiers, or a self-serve checkout flow. The pricing page appears empty in the captured text, so pricing details are not available from the provided evidence.