The integration platform is a bit of a paradox: it’s a critical part of the system architecture that delivers no easily calculated business value. As a result, migrating an existing platform to a shiny new iPaaS may seem hard to justify financially, as success often means that on the surface-level everything remains the same.
And when a migration is performed, it’s usually either due to the old solution reaching its end-of-life phase or in a high-pressure situation where there are a lot of other changes going on simultaneously, such as:
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The business has merged with another company, and multiple platforms need to be consolidated.
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An ERP-project forces changes across the integration landscape.
Integrations’ criticality to business operations, tight schedules and lack of access to key personnel have made migration projects scary or even unpredictable.
But now, AI agents promise to remove the drama. Or at least some of it.
Why integration migrations are especially unforgiving
Integration solutions age differently from other software. A typical solution has a lifespan of 5-15 years, and over that time a lot happens: the original developers move on, new integration layers are added by other people, business logic drifts away from what anyone remembers and documentation practices change.
When it’s time to migrate to a new platform, the team is expected to understand dozens or even hundreds of integrations quickly based on limited documentation and without access to the old platform, full runtime environment, or even the people who built it.
And there is rarely time for a comprehensive discovery phase.
How AI agents help iPaaS migrations
In our earlier blog post, we discussed the use of AI agents in the development of an iPaaS solution. And while everything we said there holds true, the biggest AI wins are usually gained before the development phase even begins.
1. Unboxing the mystery box of integrations
We can build AI agents that tell us what an integration does and why. The agent studies the code, configurations, parameter files – regardless of coding language – along with any available documentation and then reports on its findings.
With agents, the mystery box of integrations can be mapped out quickly and comprehensively, allowing our team to identify the specifics and catch undocumented third-party dependencies, thus preventing surprises that can derail a go-live date.
Agents don’t necessarily make the definition phase faster, but they can save a lot of time down the road.
2. Plan for the strengths of the new platform
An easy mistake is to treat a migration as a lift-and-shift exercise. After all, integrations just move data from one place to another, right?
Maybe, but older integrations often reflect the technical limitations of their time and outdated architectural choices e.g. ESB-style routing, file-based exchanges and customized technical workarounds.
Once agents have translated legacy implementations into technology-neutral descriptions, they can help us design a target state that optimizes data flows and leverages the new platform's full potential.
The agents’ role here is to accelerate comparison, exploration and option analysis, which is key to making the right decisions for the long run.
3. Robust your test automation practices
As any careful developer knows, you can never test too much prior to going live, and this is especially true for integration platforms where there are a lot of potential loose ends. But with tight schedules, teams can’t always test as much as they’d like to.
AI agents make comprehensive testing achievable. They can generate an endless supply of realistic test data, enabling exhaustive end-to-end testing that produces meaningful results. Agents can also be utilized for running the tests regularly, which helps make testing a consistent part of the process.
What are the benefits?
AI agents are like a tireless extra pair of hands that allow the team to work more meticulously than they could otherwise. The primary benefit for leadership is reduced delivery risk, ensuring the integration remains on track despite a demanding timeline.
In practice, this translates to:
More reliable estimates regardless of the environment and circumstances. This is not due to the team being able to work faster, but rather because they can do more in the discovery and planning phase.
Fewer mid-project surprises. Nothing throws a wrench in the project plans like an undocumented connection to a third-party solution. With agent-powered mapping these can be caught early.
Better use of people’s talents. Business and platform experts can focus on validating agents’ suggestions and improving on them, instead of reverse-engineering legacy solutions.
No more reliance on a single key person. Legacy solutions often have dark corners, whose logic is only known to someone who no longer works at the company. With the help of agents, any team member can shine a light on these corners and discover their purpose without having to dig up former employees.
What does agentic AI mean for the future of integration work
Complexity is in the nature of integrations. It’s what makes them both fascinating and incredibly frustrating. AI agents do not make integrations simpler, but they do strip away a lot of the frustration.
In short, agentic AI helps us build integrations the way we have always wanted to but could not due to a lack of time, money and capacity.
For users, the use of AI agents means robust, vendor-agnostic iPaaS solutions that operate exactly as they should at all times. And that when something does go wrong (that remains unavoidable), finding out what it was and fixing it is not an issue.
