Agentic AI Projects Struggling with Data Issues in Production

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Most agentic AI initiatives currently in operation face significant delays due to data-related challenges.

Survey Insights

Organizations are deploying AI agents to interact with real-time data streams, assigning them tasks that previously required manual oversight in areas such as IT management and software development. A survey conducted by Confluent’s annual Data Streaming Report, which gathered insights from 4,625 IT decision-makers across 14 nations, revealed that 32% of organizations have implemented agentic AI systems in production as of 2026, marking an increase from 29% in the previous year. The report highlights persistent hurdles that hinder widespread adoption, with governance and data quality issues emerging as primary barriers.

Top Concerns

IT leaders identified a skills shortage and insufficient organizational preparedness as the top concern, affecting 69% of respondents. Concerns regarding the reliability and non-deterministic nature of large language models followed closely at 68%, while data infrastructure and quality deficiencies impacted 66% of organizations. Governance, risk, and compliance complications affected 65% of participants.

Impact on Projects

These challenges reflect both technical and security-related complexities, as the inability to process real-time data effectively has widened existing infrastructure gaps. Persistent uncertainties about data origins, recency, and credibility further complicate implementation. Autonomous agents rely on incoming data to execute actions, creating security risks when data provenance and freshness cannot be verified. The financial and operational consequences of these issues are evident among organizations with advanced implementations. Among those operating agentic AI systems, 77% reported project delays linked to these obstacles, while 61% cited complete project abandonment as a significant problem. Common delays range from one to five months, with some initiatives ceasing entirely.

Shifting Left Strategy

Data reliability issues directly impact operational efficiency, underscoring the need for robust data management. The report emphasizes a growing focus on shifting left, a strategy that involves embedding data validation, encryption, and access controls at the point of data ingestion. This approach ensures downstream systems inherit these security measures, reducing risks associated with unverified data. Survey results indicate that 43% of IT leaders consider inline security and governance enforcement as essential capabilities for data streaming platforms, with 81% recognizing it as a major benefit. Support for shifting left practices received strong backing, with 77% deeming it mandatory or highly desirable.

Challenges and Recommendations

Across the surveyed organizations, recurring issues include untraceable data sources, outdated information, and fragmented governance across systems. These conditions create environments where autonomous agents make decisions without proper oversight. Key questions for security teams include verifying data validation points, controlling access permissions, documenting data origins, and addressing potential tampering in data streams. While investment trends indicate a clear direction toward addressing these challenges, critical risks remain unresolved. The report highlights the urgent need for improved data integrity frameworks to support the growing reliance on agentic AI systems.

According to the Confluent Data Streaming Report, 32% of organizations have implemented agentic AI systems in production as of 2026, marking an increase from 29% in the previous year.