Agent safety: task success masks data-handling failures

Singapore's AI Safety Institute and Korea AI Safety Institute released a joint evaluation in mid-August 2026 finding that autonomous agents can successfully complete assigned tasks while still exhibiting serious data-handling failures, according to Agentry.

Task Success Masks Safety Gaps

The two institutes evaluated 12 realistic agent tasks spanning enterprise productivity and customer-service workflows. The core finding: agents completed their assigned work correctly while simultaneously mishandling sensitive data, improperly storing information, or failing to enforce proper access controls. The evaluation concluded that "task correctness alone is insufficient to assess agent safety," highlighting a critical gap in how agent performance is currently measured.

This distinction matters in real-world deployments. An agent tasked with processing a customer support request might successfully resolve the ticket while leaking PII (personally identifiable information) in log files, storing credentials in plaintext, or granting unauthorized database access. From a task-completion standpoint, the agent "succeeded." From a security standpoint, it introduced material risk.

What the Evaluation Covered

The joint effort examined how agents handled realistic business workflows—the exact scenarios enterprise teams are deploying agents into today. Rather than testing agents on abstract benchmarks, the institutes stress-tested them on workflows that mirror actual corporate environments: customer interactions, data processing, access-restricted operations.

The finding reinforces a principle emerging across agent safety research: success metrics and safety metrics are not synonymous. An agent can optimize for task completion while remaining indifferent to data governance, encryption, or least-privilege access patterns. Without explicit safety constraints and evaluation, those gaps remain invisible until agents are in production.

Implications for Enterprise Deployment

The timing is significant. Enterprise adoption of autonomous agents has accelerated throughout 2026, with companies deploying agents into customer service, IT operations, and administrative workflows. Most current evaluation frameworks focus on task accuracy and speed—whether the agent completed the job. This report suggests that standard deployment checklists are missing a critical dimension.

The institutes' finding aligns with a broader pattern in agent safety research: successful task execution does not automatically translate to secure task execution. As agents gain access to real systems, databases, and customer data, separating task correctness from data safety becomes operationally essential.

For teams evaluating or deploying agents, the report underscores a practical requirement: task success metrics must be paired with explicit security and data-handling evaluations before production rollout. A completed task that leaks data is not actually completed—it is a liability.