Great in the demo. Unproven in production.

Organizations have been burned by AI agents that look great in a demo and then fail on basic, everyday situations once live. The main reason is that AI agents were never tested against the full range of scenarios or capabilities they need to perform. For example: An AI agent that can't reliably collect a date of birth, one that fails on a scenario nobody predicted, or worse, one that the organization flagged in advance but never actually tested.

That gap between a few tested actions and the full range of scenarios an AI agent needs to handle is why AI programs stall. This requires companies to run time-consuming manual reviews, spend weeks of bug-bashing, and maintain hand-written test spreadsheets, adding delays and risk to every launch. If AI agents aren't tested with enough variation for every scenario and capability they are supposed to perform, the gaps don't surface until thousands of real callers hit them.

From manual reviews to built-in evaluation

The solution is to make evaluation an automatic part of how an AI agent is built, not a separate project someone has to remember to run. Every job the AI agent needs to do, each capability and each end-to-end scenario, becomes its own test, generated directly from the AI agent's design rather than handwritten after the fact. 

For example: 

• An authentication flow where one untested edge case can block every capability downstream of it. 
• A payment confirmation step, where an unevaluated path carries direct financial and compliance risk.
• A multi-step eligibility workflow with many branching paths that are easy to under-test by hand.

By automating the AI agents' evaluation process, organizations can launch AI agents faster, with fewer production incidents, and with quality that can be demonstrated rather than taken on faith. 

How Replicant scales AI agents’ quality, powered by automation

Replicant’s AI-building-AI approach uses automation to design, test, and continuously validate the AI agents that go live, so quality scales with the AI agent instead of becoming the bottleneck that slows down the launch process.

Automating the testing and evaluation process tackles the hardest, most manual part of shipping an AI agent. Replicant can confidently launch high-quality AI agents at scale, proven through automated evaluations before go-live, so buyers move faster with less risk and far less manual UAT effort.

What's new: Automated Simulations

Replicant is introducing Automated Simulations (beta release), which automatically turns every job an AI agent needs to perform into an automated evaluation process. This is grounded in real conversations. Conversation Intelligence listens to every conversation, including the complex situations and edge cases human agents already solve. Those become part of the automated evaluations, alongside the scenarios and capabilities the AI agent is expected to handle. 

Automated Simulations uses two testing modes to cover different kinds of risk: 

• Scenario-based simulation evaluates a full workflow end-to-end (i.e., a financial services qualification-and-intake call, from start to finish).
• Capability-based simulation isolates a specific piece of it (i.e., just the authentication step, run on its own). 

Evaluations run with enough breadth and depth, and each test runs repeatedly against real-world variation: different phrasings, accents, and inputs, enough times to produce a measurable success rate that is statistically meaningful before go-live.

With Automated Simulations, Replicant can now:

• Generate the full test set from the AI agent's design. Every capability and scenario the AI agent must handle becomes a runnable test automatically, covering both full end-to-end workflows and individual capabilities.
• Run each test against real-world variation. Natural utterance variants such as local dialects, phrasings, and mistranscriptions multiply a single test into a high sample size of realistic runs.
• Report a real pass rate. Every capability and scenario carries its own measured success rate that reflects real behavior instead of a single anecdotal pass.

Automated Simulations provides the highest value for customers in regulated or high-stakes environments running high-volume, repeatable workflows, where a single unevaluated use case can mean a stalled go-live or a bad experience at scale.

How Automated Simulations succeed: Breadth, depth, and scale

Automated Simulations turns every job an AI agent has to do into an automatic evaluation process built into the AI agent. It works along three axes:

• Breadth — extensive design coverage. Every capability the AI agent needs (collecting a date of birth, authenticating a caller, taking a payment) and every end-to-end scenario (an existing customer paying a balance, a caller who fails authentication and is transferred) gets its own test. Nothing depends on a builder remembering to write it, so nothing gets missed.
• Depth — natural utterance variants. Each test runs many times against natural variation in how real callers speak: different accents, phrasings, hesitations, and inputs. One pass is an anecdote; multiple runs across that variation produce a pass rate that reflects real behavior.
• Scale — a high sample size. Enough runs to be statistically confident before a single customer is on the line. A sample size of one is meaningless when you are testing something that is probabilistic. Automated Simulations reduce the margin of error, giving confidence in a go-live decision.

Worth noting: For workflows that require precision and low latency, and that can never fail no matter what the caller says, code is the right tool instead of prompts, verified with unit tests rather than Automated Simulations.

Automated Simulations is the part of Replicant's AI-building-AI platform that proves the work. The same conversation data used to build an AI agent is used to test it, at a breadth and depth no manual process can match, and every run feeds back into improving the AI agent. As automation scales from a few AI agents to hundreds, quality is demonstrated continuously rather than re-checked by hand.

Looking to launch AI agents you can prove are ready? Connect with our team to see Automated Simulations.