.png){kind=logo}
MuleSoft's 2026 Connectivity Benchmark Report, built on interviews with 1,050 IT leaders worldwide, found that 88% of organisations are already moving toward autonomous AI agents, yet half of those agents work in isolation, and 86% of IT leaders warn that, without integration, an agent adds more complexity than value. Behind those numbers sits a quieter engineering reality: as a system splits into hundreds of services, its weak points move from inside any single one to the connections between them, where a change on one side can break a neighbour that still expects the old behaviour. Most test suites check each service on its own and treat those connections as an afterthought, so a mismatch surfaces late and expensively, long after it was cheap to fix.
Few engineers have worked that gap from both sides as closely as Aleksei Taranenko. A QA automation specialist with over eight years of experience on high-load financial and telecom systems, he has spent that time building the tests that guard the seams most suites ignore. At Ekleft, an IT integrator, he led an OTP Bank platform’s move of inter-service communication from IBM MQ to Kafka, building validation for asynchronous flows that lifted automated coverage of critical operations to roughly 80–90% and cut defects reaching production by up to 80%. Earlier, as the sole automation engineer on a Rostelecom project at RTC IT, he built its integration test infrastructure from scratch to about 90% coverage of critical scenarios. A judge at eleven international Major League Hacking events and now a Fellow of the Hackathon Raptors association, he was asked what the integration problem looks like from inside the code.
It looks like a bug that no single team can find by testing its own service. Split a system into many parts, and each part can be correct on its own while the assembled whole still misbehaves, because the behaviour that now matters lives in the exchange between components. A service returns a valid response, and a consumer two hops away reads one field the wrong way. Integration is hard not because joining two endpoints is difficult, but because the meaning of the system has migrated into the gaps, where a typical suite is not looking.
The break between the two teams’ assumptions. A unit test never touches the interface; an integration test exercises it only once everything is assembled, which is late and noisy. Contract testing sits between them, fast and early, with one side virtualised, and it comes in two forms that catch different failures. The structural form binds the contract to typed objects, staying quiet when a provider adds a field nobody reads and failing loudly when a field a consumer depends on changes type. The behavioural form compares whole messages and the order they arrive in, catching a reordered sequence or a wire-format change that the typed view would miss. A company with hundreds of services has both kinds of breaks waiting to happen, so the practical question is which one to reach for first: stable synchronous REST rewards the structural style, since the compiler becomes a free reviewer, while brokers and event streams in the critical path call for the behavioural one, because order and format are where those systems fail.
The question of whether something passed stops having an instant answer. Synchronous code asks and hears back in the same breath; an event-driven flow takes the request, splits it across several consumers, and the outcome you actually care about lands somewhere downstream a moment later. A test that waits at the front door learns nothing. So each test traces a transaction by its identifier all the way to the database row it was meant to change, and has to judge whether a missing reply means slow or broken, which on a banking platform is a distinction with money attached. The cases worth most are the ugly ones. A consumer falls behind, and the queue backs up: does the operation complete late, or quietly get lost? The broker resends a message on retry: does the payment post once, or twice? Those are the scenarios the validation had to answer for every critical operation, because wiring the broker in is trivial and proving that every money-moving operation behaves exactly as before is the real work. That proof is what the numbers in your question actually measure. The regression cycle tells the same story from the other side: what once spanned days now finishes in under five hours, which changes how often a platform can afford to release at all.
With the question of what actually hurts a customer, the coverage percentage can wait. The temptation is to chase total automation at once, which produces a brittle heap that the next change shatters. So the scenarios I lock down first are the ones whose failure a subscriber feels immediately: a line that will not activate, a charge that comes out wrong. Just as important is resisting the urge to write something clever that only its author can follow, since a framework no teammate can extend is a single point of failure in a useful disguise. Nearly all of it lives at the interface level, where a service is exercised through its outer boundary without booting the whole system, so a run stays close to unit-test speed while still flagging a contract that quietly came apart between two services.
An assumption that a system is healthy whenever its individual services are. Most teams sink their hours into the happy path of one component and file the agreement between components later; at TeamForce, where more than seventy teams tackled three cases, the builders whose work stood out did the reverse, down to naming the exact field of an interface another team relied on. Making one screen behave is cheap. Making it behave under a neighbouring service's full load decides whether the thing is still standing a year on.
It gives me a way to stay close to how the best specialists in the field are actually solving problems, not just reading about it after the fact. Fellows come out of companies like Google, Apple and Amazon, so being part of that group means trading practices and lessons directly with people who are working through the same integration challenges. It keeps me current with where the industry is actually heading, not where it was a year ago.
Treat the interface as something you write down and own, the same way you treat the code inside a service. Put contract checks in the same pipeline stage as unit tests, so a break is reported to the team that caused it instead of surfacing in a shared environment a week later. This grows more urgent as autonomous agents begin calling one another’s APIs: every AI agent is one more seam that can drift. And one thing I tell engineers starting out: get fascinated by the connections, because a system's fate is decided in the space between its services far more often than inside any one of them.
