A landmark Mimica product is redefining how organizations leverage process intelligence. Mimica Maker creates AI agents based on how your team actually works – enhancing the effectiveness, productivity, and ROI of your AI strategy.

Maker builds AI agents based on extremely rich context. Starting with a detailed, real record of how work actually happens, Maker builds an agent grounded in that unique process, ready to act and improve on it.

We’re past the point of spending unlimited money on AI that may or may not solve your problems. Maker is the missing link that uses your unique enterprise processes to determine where and what to automate, saving time and costs. 

From observing work to building agents

‍Mimica captures the way people actually do their job over a defined period: every click, keystroke, and action taken across desktop systems and applications. From there, Mapper constructs a detailed process map: every variant, every exception, every decision point a team encounters while doing the work. 

‍Maker is what turns that map into something that can act. By targeting the work most worth automating in the first place, Maker is designed to make as big of an impact as possible. Some processes can be simplified or removed. But when automation makes the difference? That’s where Maker comes in.

It generates the code for an AI agent that executes the work itself, navigating systems, reading documents, making decisions the way your best people do, and escalating exceptions when human input is needed. 

A lot of enterprise automation still starts with someone interviewing a subject matter expert or a business analyst mapping a process by hand, and both approaches carry the same risk: people describe their work the way they think they do it, not always the way they actually do it. Mimica skips that translation step entirely.

Built fast, checked carefully

Maker moves at a different pace than traditional automation development. An agent can be ready in as little as 24 hours. That speed also scales: the Mimica platform can mine thousands of tasks, generate process maps rapidly, and turn multiple workflows into agents at once.

Maker automates the steps within a workflow that are best suited for automation; that allows teams to offload repetitive, manual work to an agent while keeping people focused on the work that requires creativity and judgement. 

Before anything runs in production, your team reviews and approves a blueprint of exactly what the agent will do. Every agent is tested against real cases already observed, not hypothetical scenarios. And when an agent encounters something it hasn't seen before, it escalates to a person rather than guessing. 

Why this matters now

Enterprises are experiencing wide-spread failures of their AI programs. Much of this failure can be attributed to a lack of clarity and visibility. Agentic AI can achieve incredible outcomes, but without the right blueprint for what to do things go awry. The key to deploying effective AI agents is understanding exactly what parts of the process should and shouldn’t be automated across which systems and building the agents from that foundation.

The other pressure on AI deployment is cost. Agents are costly to build, deploy, and maintain. That makes it harder to justify automating everything and easier to justify automating well: putting effort where it returns the most, rather than spreading it thin across every process that could theoretically use it.

Maker is built for both problems at once. Every agent starts from where the automation opportunity is largest and Mapper's detailed record of how that specific process actually runs, so Maker agents show up with the context general-purpose AI pilots are missing, aimed at the work most worth automating in the first place. That's a different kind of AI adoption: not a wave of experiments hoping something sticks, but agents built once, correctly, on work that matters.

That's the promise behind Maker and its AI agents that watch and learn: not AI everywhere, but AI exactly where your business needs it, grounded in how the work actually gets done.

Want to see Maker in action? Register for our upcoming webinar on October 20 for a live walkthrough of how Maker turns process maps into agents.

FAQ

What is Mimica Maker?

Mimica Maker is an agentic automation platform that turns real-world processes into production-ready agents. Maker automatically generates AI agent code and orchestrates those agents alongside deterministic automation and human handoffs to execute work end to end. Because each automation is built from a detailed record of how work actually happens, it can navigate systems, read documents, make decisions the way your best people do, and escalate exceptions when human input is needed.

What's the difference between Miner, Mapper, and Maker?

Miner captures how work happens across an organization by observing real activity on people's desktops. Mapper turns that activity into a detailed process map, including every variant and exception a team runs into. Maker takes the parts of the map most suitable for automation, optimizes them, and generates the code for an AI agent that can run the processes. Each product feeds the next.

How is Maker agentic automation different from robotic process automation (RPA)?

RPA is best suited for repetitive, predictable tasks where the steps and rules are defined in advance. It follows a deterministic workflow and therefore struggles when something unexpected happens. Agentic automation is more adaptive: it can interpret context, make decisions, work with unstructured information, and adjust how it completes a task based on the situation.

Rather than replacing RPA, agentic automation can combine AI agents, deterministic automation, and human input, using each where it makes the most sense across a process.

How is Mimica Maker different from traditional automation discovery?

Traditional automation programs often face large backlogs, lengthy requirements gathering, and high development costs. As a result, many automatable workflows never get built, while incomplete requirements can lead to missed steps, exceptions, and rework during delivery.

Maker changes this by building automation from a detailed record of how work actually happens. Maker identifies the steps best suited for automation and generates production-ready agentic automations from them. This helps automation teams expand the pool of work they can automate, reduce the cost and effort of delivery, move faster, and start with more complete and accurate requirements.

Does Maker aim to automate every process?

No. The goal is to find where automation makes the biggest impact and target that work first. Some processes are better served by simplifying or standardizing steps before anything gets automated at all. Maker is built to go after the work most worth automating, not automation for its own sake.

How long does it take to deploy an agent with Maker?

An agent can be ready in as little as 24 hours. Because the process doesn't rely on someone writing automation scripts by hand, it scales well beyond a single workflow: the Mimica platform can mine thousands of users, generate process maps rapidly, and turn multiple workflows into agents at once.

What happens if an agent runs into something it hasn't seen before?

It escalates to a person rather than guessing. Every agent is tested against real cases already observed before it goes live, and your team reviews and approves exactly what it will do first. When something falls outside what the agent was built on, a person handles it instead.

What kinds of automation can Maker build?

Maker generates agents that combine GUI automation, API calls, and LLM reasoning, using each where it fits best: deterministic execution for routine steps, and model reasoning reserved for the judgment calls that actually need it.

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