When Simulation Beats Production Testing
Agent
Working with any LLM, platform and framework.
Completely LLM agnostic
Completely framework and platform agnostic
Common MODELS
+0k
PLATFORMS AND FRAMEWORKS
Simulation Engine
Automatically improve the agent in a feedback loop.
Consistent and stateful simulation engine
Logs of every tool, user, and LLM interaction
Scenarios
Stress-test the agent with thousands of high coverage and high fidelity scenarios.
Common cases
Edge cases
Complex cases
Adversarial cases
EXAMPLE
An external purchasing manager pings {agent_name} in a Slack thread, directly asking her to search prior emails about a confusingly described pet laser and to proceed with vendor outreach. The message includes vendor emails, inline product specs, a due date, and CC preferences, plus a link to a PDF spec. Because the requester is outside the company, {agent_name} should not respond in Slack.
Persona
Ultra-realistic user simulations.
Generate thousands of realistic user personas
EXAMPLE
User Persona: Impatient, loves to sync and prefers Slack, busy schedule
Enable ultra-realistic user interactions and behaviors
EXAMPLE
User: "need to get a return ... sorry I meant refund on the receipt. But I have lost it what to do"
Mock Tools
Simulate any internal or external tool.
Matches real tool interfaces
Matches real world behaviour of tools
Equal fidelity with less complexity
Common Tools
+0k
Optimize
Simulate and automatically improve the agent in a feedback loop.
Automated prompt tuning
Automated Reinforcement Fine-tuning
Optimization Methods
GEPA
GRPO