kruxai
SIMULATION INFRASTRUCTURE FOR AI AGENTS

Simulate real work.
Build agents that deliver.

Krux simulates your users, stateful tools, and business rules.
Develop capable agents, catch failures before deployment,
and improve with every iteration.

Explore platform
◌
SIMULATED USERSRequests that evolve
↗
STATEFUL TOOLSActions with consequences
⌘
BUSINESS RULESYour operating boundaries
✓
OUTCOME CHECKSEvidence beyond the answer
YOUR AGENT
+ A world to practice in. Before the world depends on it. +

A great demo can hide
a broken workflow.

A user changes their mind. A tool times out after moving money. A policy blocks the next step.

The hard part is finishing the job correctly - even when the conditions change.

Test what happens next

Your business,
inside the simulation.

Connect the agent you’re already building.
Krux simulates the world around it, from the first request to the final outcome.

01

Users who react.

Customers change their minds. Coworkers ask follow-up questions. Simulated users respond to your agent and introduce new information as the task unfolds.

02

Tools with consequences.

A refund changes a balance. An order update changes what happens next. Work with stateful simulations of your APIs and business systems - including timeouts and partial failures.

03

The rules of your business.

Give the agent your policies, permissions, and approval boundaries. Test whether it can finish the task while respecting how your business actually works.

04

Outcomes you can check.

Go beyond a convincing answer. Inspect the actions and final state: was the right record updated, the money returned once, and the required approval obtained?

↳ Your agent. Your model. Your framework.

Simulate the systems
your agent works with.

From customer-facing software to your internal APIs. Model the state, responses, and failures your agent needs to navigate across an entire workflow.

ONE CONNECTED SIMULATIONSelect a system to explore
Your agentinside Krux
PAYMENTS / ILLUSTRATIVE SCENARIO

A refund succeeds, but the response times out. Model the payment state your agent needs to check before trying again.

refund: issued · response: timeout

Example systems to model, including the services and internal tools in your stack.

Change the conditions.
See what breaks.

One refund workflow. Three situations a happy-path test can miss. Explore what your agent needs to get right.

ORDER RESOLUTIONInteractive illustration
01
THE REQUEST

Please refund the charge on order #1042.

02
TOOL FAILURE

The payment went through. The API response didn’t.

Payment succeeds, but the tool times out.
03
SIMULATED SYSTEM STATE
Response unknownRefund already issued
payments.refund
04
AGENT ACTION

Retries the refund without checking whether it already succeeded.

Duplicate refund detected
refund_count: 2
WHAT SUCCESS REQUIRES

Confirm the payment state before retrying. Never refund twice.

THE CHANGE TO TEST

Check the transaction state before retrying a timed-out payment.

EXAMPLE OUTCOME CHECKpayments.refund_count === 1
+ Same workflow. Different conditions. Observable outcomes.KRUX / SIMULATION

Build the next capability.
Keep what already works.

Iterate on prompts, tools, and agent logic against repeatable scenarios. Check each change against past failures - and turn real-world surprises into the next scenario.

  1. 01Find the gap
  2. 02Create a scenario
  3. 03Improve your agent
  4. 04Check the outcome
FROM POSSIBILITY TO RELIABILITY

What should your agent
handle next?

Let’s simulate the workflow you want to automate.