TFiR

AI coding assistants ship functions, services, and entire applications with no instrumentation attached. When those apps hit production and break, teams have no traces, no spans, and no business-logic context to hand a remediation agent. Bolting on observability after the first outage is not a strategy; it is a recovery tax paid in downtime and engineering hours.

Teams scaling AI inference globally are forced into a cost discipline decision before they have clear answers: optimize for user growth and mindshare, or optimize for margin and unit economics. Choosing the wrong framework does not just hurt finances; it produces the wrong architecture, the wrong metrics, and the wrong product decisions. There is no neutral middle ground once inference workloads begin to scale.

SOC analysts are fielding thousands of alerts per shift while threat actors use AI to automate reconnaissance, generate polymorphic payloads, and scale spearphishing with a precision that human-speed triage cannot match. The tuning trade-off inside most SIEMs has defaulted to surface everything rather than miss anything, which means the alert queue never shrinks.

Enterprises deploying AI agents and stateful applications are running into a hard infrastructure ceiling. Vanilla Kubernetes provides no data service orchestration layer, and without one, AI workloads that carry state have no reliable foundation to run on across multi-cluster environments.

AI agents are useless without access to enterprise data, but unrestricted access breaks compliance, violates data sovereignty requirements, and makes audit trails impossible. Write-enabled agents acting inside production systems with no permissioning layer create accountability gaps that most enterprises cannot accept. These three problems block nearly every serious enterprise AI agent deployment before it reaches production.

Running AI workloads across siloed observability, search, security, and APM stacks creates an unmanageable data tax, compounding security risk and unpredictable cost at scale. As organizations push toward hundreds of thousands of queries per second, those architecture decisions made before this generation of AI become critical liabilities.

CData Software has launched Connect AI Gateway, a new control layer designed to govern how AI agents and employees interact with enterprise applications and data. The platform aims to address a growing enterprise challenge: as AI moves from generating answers to executing tasks, organizations need consistent controls over permissions, context, model selection, cost and the actions agents take.

AI agents taking autonomous action inside enterprise systems produce wrong answers and risky outcomes when they lack enterprise context. Without knowing how a company defines its own terms, where its data lives, or who is permitted to access what, an agent filling a gap in its knowledge does not say it cannot answer. It invents one. Accuracy benchmarks show this clearly: agents operating against live enterprise data sources without a context layer achieve around 65% accuracy.

Agentic AI systems must make hundreds of autonomous decisions per second across live business processes. Centralized infrastructure built for model training cannot meet that requirement. The latency ceiling that was acceptable for chatbots becomes a hard failure boundary when AI agents replace real-time workflows previously handled by humans or deterministic algorithms.

Token costs do not scale linearly. Routing every task through a multi-trillion-parameter model feels safe in development and becomes financially catastrophic in production. Most teams discover this too late, after architecture decisions are already locked in.