Legacy System

A typical example: industry-specific software for inventory management or order processing that's been running reliably for years but isn't being developed further and offers no external connections. Replacing it is expensive, risky, and often operationally impractical — so the system stays, along with all the manual work that's accumulated around it.

The good news: automation doesn't require a system swap. The legacy system can remain the core while AI agents handle the manual data entry and transfer work that's built up around it.

AI agents work with legacy systems even without APIs: through file exports and imports (CSV, Excel), by reading generated documents and reports, via email interfaces — or by operating the user interface like a person would. Which approach makes sense depends on system stability, data volume and frequency of changes; often the straightforward file-based approach is the most robust.

The classic big-bang approach — switching off the old system, rolling out the new one, migrating all data — ranks among the riskiest IT projects: long timelines, high costs, and operations must continue running. The alternative is gradual transformation: the legacy system remains the core initially while new functions and automation build up around it. Piece by piece, individual tasks migrate from the old system to modern components until the core eventually shrinks enough to be safely retired — or until it becomes clear it can keep running as is.

AI agents fit this strategy in two ways: in the short term, they remove the manual work surrounding the legacy system and free up operational capacity. Medium-term, they document the actual processes and data flows through their work — exactly the knowledge that would otherwise need painstaking reconstruction before a future system decision. Those who automate first and migrate later make system decisions based on real process data instead of guesswork.

The decision to connect a legacy system ultimately comes down to one simple question: does the long-term saving in manual effort exceed the cost of building and maintaining the connection? Several factors regularly favour a connection: high daily volume of manual tasks, stable processes with few exceptions, and infrequent changes to the legacy system. The more stable the system, the less maintenance effort required — this significantly improves the business case.

Arguments against investing in complex integration include an anticipated system change in the near future or highly irregular processes where the effort to handle exceptions outweighs the benefit. In such cases, a lean interim solution makes more sense: reduce manual effort through partial automation without creating deep dependencies on the legacy system. The most honest basis for this decision is structured measurement of actual work effort — not gut estimates, but measured minutes per transaction multiplied by frequency. This figure makes the decision objective.

Old systems bring their own security dimension that must be thoughtfully addressed when creating connections. Legacy applications often run on operating systems or database versions that no longer receive security updates — they're stable but vulnerable. When automation connects to such a system, this link should be as tightly constrained as possible: preferably through a secured, isolated channel rather than a wide-open access point. The goal is not to further expose the legacy system.

A common, unremarkable approach is often the most secure: exchanging data through files in a clearly defined, monitored directory. The agent reads exports and writes imports without directly accessing the vulnerable system — the attack surface stays minimal. Where direct connection is necessary, the same principles apply as with any integration: dedicated technical access with minimal permissions, encrypted connections where possible, and logging of all access.

Don't overlook the data protection angle: legacy systems often contain historically accumulated, extensive datasets. Before granting an agent broad access, it's worth clarifying exactly which data it needs — and then access is restricted accordingly rather than opening everything up.