Operations | Monitoring | ITSM | DevOps | Cloud

Every enterprise IT leader facing the spiraling complexity of modern IT environments has a version of the same conversation. How can we manage the increasing complexity of more services, more dependencies, and more layers of observability and monitoring? Their answer would add headcount to the NOC, sign another Global System Integrator contract, and buy your organization another year.

Database changes are inherently complex: coordinating schema updates, managing risk, and avoiding downtime all require care. Even when teams improve how they deliver those changes, governance often remains inconsistent, manual, and reactive. In many environments, governance is treated as a separate layer around deployment. Policies are applied unevenly, approvals become bottlenecks, and audit evidence is assembled after the fact, creating gaps in enforcement and increasing operational risk.

Every agent demo follows the same arc. The agent calls an API. A deployment triggers. A ticket gets created. The audience is impressed. Then someone asks a real question: "Which regions had the highest order failure rate this quarter, and are any of them linked to vendor SLA breaches?" That question crosses four entity types — orders, fulfillment records, vendors, SLA contracts.

Key takeaway: Hyperscaler pricing models often penalize e-commerce growth due to unpredictable egress fees and unbounded auto-scaling, but moving to a resource-based allocation model allows teams to treat infrastructure costs as a deliberate business decision rather than a post-campaign surprise. Ecommerce traffic doesn't grow linearly. It spikes, and every spike rewrites your cloud bill.

Agentic workloads thrive with precision tooling. Just like developers, they need the rich context, high cardinality, and fast feedback loops that allow them to ask exploratory open-ended questions of their code. But instrumentation is costly, and from the dawn of software, developers have tried to do the most possible with the least amount of resources.

Discover Aiven for DataHub: a fully managed, open-source data catalog that gives your teams and AI agents the context they need to find and understand data. According to an MIT study, 95% of AI projects fail to deliver value. I've been thinking about why that number is so stubbornly high, and I've come to believe the answer isn't about models,compute or even data quality in the traditional sense -It's about context.