Why Agentic Operations Make or Break Enterprise AI Success
If you’re a CIO today, you’re likely dealing with two very different stories about AI agents.
Two Perspectives on AI Agents
The first is optimism. Teams can stand up agent prototypes in days. Demos are compelling. Agents summarize documents, automate workflows, and interact with users in ways that feel genuinely transformative.
The second story is quieter but far more important. Very few of these agents are making it into mainstream production. Even fewer are operating reliably, securely, and economically at enterprise scale.
Our global research confirms this gap. While 95% of enterprises have some form of agentic AI in production, only 13% have deployed more than ten agents supporting core business functions. That 13% is not just ahead – they are fundamentally different. They achieve 2.5x higher ROI from their agentic initiatives and show a clear flywheel effect, planning to add an average of five more production-grade agentic domains in the next year. The remaining 87% struggle to move from four to five.
The Importance of Sovereignty
The difference is not ambition or talent. It’s AI and data sovereignty.
The enterprises thriving with agentic AI have made one thing non-negotiable: sovereignty over their AI and data – secure, compliant, and operable anywhere, anytime.
Sovereignty is not a geopolitical talking point. For CIOs, it is an architectural and operational principle. It ensures control over data, models, decisions, costs, and compliance. It addresses four critical challenges that determine whether agentic AI scales – or stalls.
Factors Affecting Agentic AI Success
Factor One: Prototypes Don’t Survive Enterprise Reality
Building an AI agent has become relatively straightforward. However, operating one inside an enterprise with real users, real data, and real risk is not.
Most failures occur because prototypes were never designed for production realities: evolving regulations, security scrutiny, unpredictable costs, and complex data estates. Sovereignty enforces a simple rule: nothing reaches production unless it is secure, compliant, observable, and operationally manageable by design.
Factor Two: Agents are Adaptive
Agents are not deterministic systems. They change behavior as data changes. This adaptability makes them powerful but also risky without the right controls.
Sovereign AI and data foundations ensure that these “living systems” remain viable over time, not just at first deployment.
Factor Three: Observability is Not Optional
Operating agents without full visibility is like driving an F1 car blindfolded. Agentic systems require heuristic observability – understanding not just performance metrics but decision paths, data usage, cost behavior, and outcomes.
Sovereignty enables this by ensuring enterprises have full visibility into their data and AI operations, regardless of where they run.
Factor Four: Scale Demands a New Operational Paradigm
Agentic scale is fundamentally different from traditional application scale. These systems must learn, collaborate, and improve while remaining secure, compliant, and auditable.
This requires a new agentic operations model that is omni-data by design, open, agile, and capable of near-infinite scale within a sovereign AI and data environment.
The Role of EDB Postgres® AI
At the center of successful agentic operations is the data platform. EDB Postgres AI (EDB PG AI) provides a unified foundation where transactional data, analytics, and AI workloads converge under a single, governed Postgres-based platform.
This matters because it enables consistent security policies, observability, and performance across the entire agent lifecycle.
Instead of copying data into fragmented AI pipelines, agents can work directly against trusted Postgres data, combining vector embeddings, relational context, and real-time analytics. This reduces data movement, simplifies governance, and improves reliability.
Those succeeding with agentic performance are taking a deliberate approach – building sovereign, open-source platforms designed for compliance, observability, and scale.
The CIO Takeaway
If you want success with your CEO’s agentic agenda, the foundational step is clear: become your own sovereign AI and data platform.
Sovereignty, embedded governance, and new agentic operations models are no longer optional. They are indicators that your organization is built not just to experiment with AI agents but to run them safely, economically, and at scale.
Because building agents is easy. Operating them successfully is the real test of enterprise leadership.
