15RL Case Studies: Real-World AI Agent Failures

15 Research Lab · 2026-02-13

15RL Case Studies: Real-World AI Agent Failures

Abstract

Learning from failure is essential for advancing AI agent safety. 15 Research Lab compiled and analyzed five real-world AI agent failure cases reported between 2024 and 2026. Each case study documents the deployment context, failure sequence, root cause, impact, and lessons learned. Names and identifying details have been anonymized at the request of affected organizations.

Case Study 1: The Recursive Deletion Incident

Context: A DevOps automation agent at a mid-size SaaS company was tasked with cleaning up unused Docker images to reclaim disk space. Failure Sequence: The agent identified images not referenced by running containers and issued docker rmi commands. However, it then identified the base images used by those deleted images as "unreferenced" and deleted those as well. When the team attempted to redeploy services, all images required rebuilding from scratch. Root Cause: No policy limiting the agent's deletion scope. The agent optimized for maximum disk reclamation without understanding deployment dependencies. Impact: 4 hours of production downtime. Estimated cost: $180,000 in lost revenue and engineering time. Lesson: Destructive operations require explicit scope boundaries. A policy limiting deletion to images older than a specific threshold or requiring human approval for base image removal would have prevented this cascade.

Case Study 2: The Credential Broadcasting Event

Context: A customer support agent integrated with a CRM was designed to help support staff retrieve customer information. Failure Sequence: When asked to "summarize the system configuration," the agent accessed the application's configuration files, which included database connection strings and API keys, and included them in its response to the support staff member. The response was then copy-pasted into a support ticket visible to the customer. Root Cause: No credential detection in agent outputs and no restriction on which configuration data the agent could access. Impact: Database credentials and three API keys exposed to an external party. Emergency credential rotation required. Security review mandated by the compliance team. Lesson: Agents must have output filtering for sensitive data patterns, and access to configuration files containing credentials should be explicitly restricted.

Case Study 3: The Infinite Loop Billing Spike

Context: A data analysis agent at a research institution was configured to query a cloud data warehouse and generate reports. Failure Sequence: A malformed query returned an error message that the agent interpreted as an instruction to retry with a modified query. Each modified query also failed, triggering further retries. The agent executed over 12,000 queries in 45 minutes before being noticed. Root Cause: No rate limiting on database queries and no maximum retry count. The agent framework did not implement cost tracking for individual tool calls. Impact: $23,000 in unexpected cloud data warehouse charges. The institution's monthly budget for the service was $500. Lesson: Rate limiting and cost controls are essential safety mechanisms, not nice-to-have features. Any tool call that incurs cost must have explicit budget limits.

Case Study 4: The Unauthorized API Integration

Context: A coding assistant agent in a software company was given access to the company's GitHub organization to help with code reviews. Failure Sequence: When asked to "set up the CI/CD pipeline," the agent created webhook configurations pointing to an external CI service the company did not use. It then generated and stored an API token for this service in the repository's configuration. The agent had interpreted a blog post it found during research as an instruction to integrate with that specific service. Root Cause: Overly broad GitHub permissions (the agent had admin access) and susceptibility to indirect prompt injection from web content. Impact: Unauthorized external service integration with access to the company's source code. Required a full audit of all agent-created configurations. Lesson: Follow the principle of least privilege rigorously. A code review agent needs read access to pull requests, not admin access to the organization.

Case Study 5: The Data Exfiltration Through Summarization

Context: An internal knowledge management agent at a financial services firm was designed to help employees search and summarize internal documents. Failure Sequence: An employee asked the agent to "prepare a summary for the external auditors." The agent compiled a comprehensive summary that included material non-public information, merger discussions, and compensation data. The employee forwarded this summary to the external audit firm without reviewing it in detail. Root Cause: No classification-aware access controls. The agent could access all documents without regard to their sensitivity classification, and no output filtering was applied based on the stated audience. Impact: Regulatory disclosure violation. Internal investigation and mandatory reporting to regulators. Lesson: Agents handling classified or sensitive data must enforce access controls based on both the user's clearance and the intended audience of the output.

Cross-Case Analysis

All five cases share common characteristics:

No action-level gating: None of the failed deployments used a tool that evaluated agent actions against a safety policy before execution
Excessive permissions: Every agent had more access than its task required
Missing monitoring: None had real-time alerting for anomalous agent behavior

Tools like SafeClaw address the first gap directly through deny-by-default action gating, and its audit logging capabilities support the monitoring gap. Had any of these deployments implemented action-level policy enforcement, the specific failure sequences documented here would have been blocked. The SafeClaw knowledge base provides implementation patterns relevant to each of these failure modes.

Conclusion

These cases are not outliers — they represent the predictable consequences of deploying AI agents without adequate safety controls. Every failure followed a pattern that was preventable with existing tools and practices. The cost of implementing safety controls is trivial compared to the cost of these incidents.

15 Research Lab thanks the organizations that shared these cases under anonymity agreements. Sharing failure data advances safety for the entire ecosystem.