Your distributed tracing stack was built to follow a request from an API gateway through a chain of microservices and back out again. It does that job beautifully. But in 2026, your backend is no longer just running microservices. It is orchestrating AI agents that plan, delegate, reason, and retry,
Something quietly broke in enterprise AI deployments late last year. Teams that had carefully built multi-agent pipelines on centralized orchestration platforms started hitting walls, not just performance walls, but architectural ones. A single orchestrator routing 60, 80, or 100 concurrent agent workflows began to look less like a control tower
Imagine you've just onboarded a new employee. They're brilliant, fast, and tireless. But on their very first day, you hand them the keys to every system in the building, full access to your payment processor, your customer database, your email platform, and your third-party logistics API,
There is a quiet crisis unfolding inside enterprise legal and engineering departments right now, and most backend teams are only discovering it after the damage is done. For the better part of the last two years, organizations raced to deploy multi-agent AI systems. The pitch was irresistible: automate complex workflows,
Something fundamental shifted in early 2026. The question inside most enterprise engineering orgs stopped being "should we experiment with AI agents?" and became "how do we stop our AI agents from taking down production?" That is not a small distinction. It signals that multi-agent systems have