What is simulation-based evaluation?

Instead of scoring a static dataset, Arklex creates the test data for you. It generates multi-turn conversations between synthetic users and your agent, then evaluates how the agent handled each turn. The result is coverage for failure modes you would not catch with single-turn benchmarks.

How is this different from other evaluation tools?

Most tools need you to bring your own test conversations. Arklex generates them. That means you can test for scenarios that have not happened in production yet, including edge cases where users push back, change their mind, or ask unexpected follow-ups.

Why does multi-turn testing matter?

An agent can ace a single question and still fall apart in a real conversation. Context gets lost by turn five. Tool calls break when the user changes direction. The agent contradicts something it said two turns ago. These are the failures that reach production, and they only show up when you test across multiple turns.

What agents and frameworks are supported?

Any agent, any framework. If it exposes an HTTP endpoint, speaks the A2A protocol, or is a Python class, Arklex can test it. The platform handles the simulation and evaluation regardless of how your agent is built.

Can I integrate this into my development workflow?

Arklex works as a CI/CD quality gate that runs on every code change, and as a standalone platform for testing, governance, and deployment approval. Teams typically start with ad-hoc testing during development and add CI gates once they have a baseline.

Workspaces are fully isolated with separate data storage. The platform can run on your infrastructure, keeping all conversations and evaluation data in your environment. Private cloud deployment is available for enterprise customers.

From Proof-of-Concept to Profit: How AI Shopping Agents Are Transforming Global Commerce

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From Proof-of-Concept to Profit: How AI Shopping Agents Are Transforming Global Commerce | Arklex.AI