Enterprise AI agents face critical gaps in evaluation, context, and security despite widespread deployment

Enterprises are accelerating AI agent deployments, granting these systems growing autonomy in critical workflows. However, recent surveys across over 100 organizations expose persistent and interconnected gaps in agent evaluation, context integration, and security controls.

More than half of surveyed enterprises have faced confirmed security incidents or near-misses involving AI agents, often due to inadequate identity scoping and credential sharing. Despite these risks, only about one-third of organizations assign scoped identities to agents, exposing systems to potential breaches and data leaks.

On the evaluation front, many enterprises acknowledge that internal testing methods fail to reliably predict real-world performance. Half have shipped AI agents that passed evaluations but subsequently failed in production environments. This misalignment between test conditions and operational reality undermines trust and complicates risk management.

Context provisioning for AI agents—critical for accurate and relevant outputs—is also a challenge. While retrieval-augmented generation has become the de facto approach, enterprises report a trust deficit in the rapidly evolving context infrastructure. Many organizations are still actively building and refining these systems to close this gap.

These findings signal that the AI agent ecosystem in enterprises is maturing faster than the governance and reliability frameworks needed to safely harness their potential. Organizations must prioritize robust security models, realistic evaluation protocols, and trustworthy context architectures to avoid costly failures and security breaches.

Looking ahead, the industry needs standardized best practices that ensure AI agents operate with reliable context, rigorous evaluation, and secure access controls. Without these, the rush to deploy autonomous AI risks undermining both user trust and operational resilience.