You're staring at logs from a customer complaint. Your AI agent, which worked perfectly in testing, just tried to purchase the same item three times. "But the demo worked," you mutter to yourself.

If you've shipped an AI agent to production, you know this feeling. The demo works. The happy path works. Then reality happens.

The Problem: "I can't write unit tests for something that's probabilistic"

Traditional software testing assumes determinism. Given input X, you expect output Y. Every time. Write a test, run it a thousand times, get the same result.

AI agents break this assumption. Same input, different output. Sometimes it works. Sometimes it doesn't. Sometimes it does something creative you didn't expect. Unit tests feel useless when the system is fundamentally probabilistic.

But here's what makes it worse: AI agents need thousands of test runs to catch edge cases.

Think about it. Your agent works 97% of the time. That sounds great until you realize you won't see the failure case unless you run it at least 30-50 times. And that's just for a 3% failure rate. What about the edge case that happens 0.5% of the time? You need hundreds of runs to find it.

The Catch-22: "We can't make 100 real purchases to test"

So you need thousands of test runs. But your agent does real things:

• Makes actual purchases
• Sends real emails
• Updates live databases
• Calls paid APIs
• Books real appointments

You can't run your agent 1000 times against production. Your CFO will hunt you down. You can't even run it 100 times. And forget about testing that "cancel order" edge case by actually canceling real orders.

This is the fundamental problem with AI agent testing: You need massive test volume, but you can't afford the side effects.

What People Try (And Why It Doesn't Work)

Approach 1: "Just test it manually a few times"
You test the happy path. It works. You ship it. Then the Shopify API returns a weird edge case response and your agent loops infinitely. A customer caught it before you did.

Approach 2: "Let's mock everything"
You spend weeks writing mock responses for every API. Now your tests pass but they're so divorced from reality that they don't catch real issues. Your mocks don't match what the actual API returns.

Approach 3: "We'll use a staging environment"
Great idea, except:

• Not every API has a staging environment
• Staging doesn't have the weird edge cases production has
• You still can't run 1000 tests without cleanup chaos
• Rate limits are often stricter in staging

The Solution: Simulation Testing

Here's what you actually need:

1. Run thousands of test iterations to catch probabilistic failures and edge cases
2. Zero real-world side effects so you can test freely without consequences
3. Realistic API responses including edge cases and error conditions
4. Understand the distribution of outcomes - not just "does it work?" but "how often does it work?"

This is what Simvasia does. We call it simulation testing.

Simvasia is built for the Model Context Protocol (MCP), the open standard for connecting AI agents to external tools and services.

Instead of connecting your agent directly to production APIs, you connect to MCP servers hosted on Simvasia. These servers can operate in two modes:

Create test scenarios with predefined responses. Not just "successful purchase" - also:

• "Out of stock" scenario
• "Payment declined" scenario
• "API timeout" scenario
• "Invalid product ID" scenario

Run your agent 1000 times against the scenarios. See exactly how it handles each case. No side effects. No costs. No cleanup.

Point the MCP server to a staging environment. Run realistic integration tests without touching production. Great for testing the full flow once your mocked tests pass.

The Answer You Can Finally Give Your PM

"How reliable is our agent?"

Before: "I tested it twice and it works."

After: "It succeeds 97% of the time. The 3% failures are all payment declines, which we handle gracefully. We tested it 1000 times in a simulation that included 15 different error scenarios."

Getting Started

Simvasia works with any AI agent framework that supports MCP. No vendor lock-in. No complicated setup. If your agent can connect to an MCP server, it works with Simvasia.

For AI Agent Developers:

1. Find the MCP server you need (or create your own)
2. Create test scenarios (mocks) for different conditions
3. Connect your agent to the Simvasia-hosted MCP server
4. Run your test suite 1000 times
5. Analyze the distribution of outcomes
6. Fix issues and repeat

For MCP Developers:

Upload your MCP server to Simvasia. Create example mocks. Make it trivially easy for AI developers to test against your tools. Drive adoption by removing testing friction.

Start testing your agents in simulation →

Built for developers who ship. By developers who have shipped (and debugged at 2 AM). Questions? Reach out on Threads, Twitter, or GitHub.