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2 min read time
A new report from Gartner delivers a sharp wake-up call for enterprises pursuing agentic AI: by the end of 2027, more than 40% of these projects will be abandoned before they reach production.
While excitement around autonomous agents is fueling innovation across industries, Gartner’s forecast reveals that ambition alone isn’t enough. What’s missing is a serious commitment to quality, from data foundations to system oversight.
Agentic AI projects are failing before they launch
As enterprises race to adopt AI agents that can plan, execute, and adapt in real time, many are discovering that their existing infrastructure simply can’t support the complexity.
According to Gartner, many organizations fall into the trap of “agent washing”—branding conventional chatbots or task automations as agentic AI without the orchestration, autonomy, or coordination that true agentic systems require.
When these overpromised solutions underdeliver, projects stall, burn through budgets, or disappear.
Gartner also says that agentic AI can’t succeed without robust management systems, risk mitigation frameworks, and ongoing performance monitoring. AI agents require high-quality, timely, and contextualized data, and that’s where many enterprises fall short. When agents act on outdated or incomplete data, the results can range from inefficiencies to outright failures.
And because AI agents are often given autonomy, the margin for error becomes smaller and more consequential.
Scaling to multiple agents raises the stakes
The stakes get even higher as organizations move from single-agent deployments to multi-agent ecosystems. This transition is where the quality issue intensifies. In a multi-agent setup, agents must not only reason independently, but also share knowledge, coordinate actions, and respond to changing environments as a team.
If even one agent is trained on inconsistent data or operates on lagging signals, the entire system becomes vulnerable. The result is misalignment, duplicated work, errant decisions, or security gaps. Without unified data governance and a strong orchestration layer, AI agents quickly become brittle.
These challenges require a shift in how enterprises prepare for agentic AI. Success is about building the right infrastructure to support autonomous systems over time. That means managing data across its full lifecycle, designing real-time feedback loops, and maintaining human oversight over systems that are designed to think for themselves.
A frontier AI data foundry platform can help
A frontier AI data foundry platform keeps AI agents working, improving, and trustworthy by providing a foundation to support the full lifecycle of autonomous systems. Centific’s approach enables businesses to:
Unify fragmented data into clean, contextualized formats ready for agent consumption.
Annotate data with expert input for model training.
Stream data in real time to keep agents current.
Simulate rare cases using synthetic data Monitor agent performance and update systems over time.
Develop and connect multiple AI agents efficiently and safely for business impact.
This infrastructure allows AI agents to adapt to real-world environments, operate safely, and deliver value.
Learn more about Centific’s frontier AI data foundry platform.
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