How AI Agents Work With Legacy Software

Minicor is the infrastructure layer that gives AI agents access to legacy desktop software. When an agent needs to read from or write to a system with no API, Minicor wraps that workflow in a clean API endpoint the agent can call like any other tool.

The gap no one is talking about

AI agents are getting powerful fast. They can reason, plan, use tools, and execute multi-step workflows autonomously. But there is a problem almost every team building agents runs into: the software that actually runs their business was never designed to talk to an AI.

CDK doesn't have an agent-friendly API. Reynolds & Reynolds doesn't. Most EHR systems don't. Neither do the dozens of other legacy desktop platforms that industries like automotive, healthcare, finance, and government run on every day.

You can build the smartest agent in the world, but if it can't touch the software your business runs on, it can't do the job.

Minicor fixes that.

What is an AI agent?

An AI agent is software that can perceive its environment, make decisions, and take actions autonomously, without a human clicking through each step. Agents typically work by calling tools: a search tool, a database tool, a calendar tool, an email tool.

Each tool is an API endpoint. The agent sends a request, gets a result, and uses that result to decide what to do next.

The problem is that most of the world's most important software that has the data the agents need doesn't have an API endpoint to call.

Why legacy software blocks AI agents

Legacy desktop software was built before the modern web, before APIs were standard, and before anyone imagined an AI agent would need to interact with it. These systems store critical data but they expose none of it programmatically.

To get data in or out, someone has to open the application, navigate the UI, and do it by hand every time.

Agents can handle everything around the legacy system but get stuck when they need to read or write from the system.

Minicor removes that bottleneck.

How Minicor gives AI agents access to legacy software

Minicor records the desktop workflow and deploys it as a self-healing automation behind a standard API endpoint.

Your agent calls the endpoint and Minicor opens the application, executes the workflow, and returns the result. The agent never needs to know what happens inside — it just gets the data.

What this looks like in practice:

• An agent managing a dealership's inventory calls Minicor to pull the latest vehicle list from CDK, without anyone logging into CDK
• An agent routing patient referrals calls Minicor to read a record from Epic and write the outcome back, without touching the Epic UI
• An agent processing applications calls Minicor to enter data into a government portal, without a human copying and pasting

The agent treats Minicor like any other tool in its toolkit and the legacy software never knows an AI was involved.

What makes Minicor different from standard RPA for AI workflows

Traditional RPA tools were built for human-designed workflows that run on a schedule. They were not built to be called on demand by an agent, to handle variable inputs from a language model, or to self-heal when something breaks mid-run.

Minicor is built for the agent era:

• On-demand API trigger — agents call when they need to, not on a schedule
• Works with variable agent inputs — handles the unpredictability of LLM-generated requests
• Self-heals when UI changes — no paging an engineer when a button moves
• Video replay + error logging per run — full observability for every execution
• Pay per successful execution — aligned with agent-driven, event-based workloads

When an agent calls a Minicor endpoint, it gets a guaranteed result or a structured error it can reason about.

The infrastructure layer agentic AI has been missing

Most discussions about AI agent infrastructure focus on memory, planning, and tool orchestration. What gets overlooked is the layer underneath: how agents actually interact with the software systems that contain real-world data.

What solves it is a reliable, self-healing bridge between the agent's toolchain and the desktop software the business runs on. That's what Minicor is.

In our experience building workflows across automotive, healthcare, and enterprise software, the teams that move fastest are the ones that stop waiting for legacy vendors to release APIs and start wrapping those workflows in endpoints themselves.

Use cases: AI agents + Minicor

Auto dealerships. Agents that manage inventory, process deals, or route leads need to read from and write to DMS platforms like CDK and Reynolds & Reynolds. Minicor gives those agents clean endpoints for every workflow, without waiting for CDK to build an integration.

Healthcare. Agents handling prior authorizations, referral routing, or patient intake need access to EHR data. Minicor wraps those EHR workflows in HIPAA-aware automations the agent can call reliably.

Financial services. Agents processing applications, verifying information, or updating records in legacy core banking systems can use Minicor to interact with software that hasn't been touched in twenty years.

Enterprise operations. Any agent that needs to touch a legacy internal tool — like a custom Windows app, an old CRM, or a government reporting portal — can use Minicor as its interface.