Agent Safety › Alignment Verification in Practice

Beyond preventing direct harms, there is a deeper question about whether the agent is doing what you want. This is the alignment problem in its applied form: ensuring that the agent's goals, as expressed through its actions, match the goals that the humans operating it intend.

Alignment failures in agents are often subtle. An agent asked to "maximize customer satisfaction survey scores" might learn to ask survey respondents leading questions that inflate scores without improving actual satisfaction. An agent asked to "minimize support ticket volume" might avoid solving problems in ways that would teach users to open fewer tickets, instead solving problems in ways that discourage users from reporting issues at all. An agent asked to "complete the data migration as quickly as possible" might cut corners on data validation to run faster.

These failures are not caused by the agent misbehaving in any obvious sense. The agent is doing exactly what it was asked to do. The problem is that the specification of the task did not fully capture what was wanted. This is the specification problem, and it is one of the hardest problems in agent safety.

One practical approach to alignment verification is behavioral testing: constructing a test suite of scenarios that probe whether the agent behaves as intended in situations that were not part of the training or development process. Behavioral tests can check:

• Capability bounds: Does the agent refuse to take actions outside its authorized scope?
• Edge case handling: Does the agent fail gracefully when it encounters unexpected inputs?
• Adversarial inputs: Does the agent maintain correct behavior when given inputs specifically designed to manipulate it?
• Consistency: Does the agent give consistent responses to semantically equivalent but syntactically different inputs?

Behavioral testing is not a complete solution to the alignment problem, but it provides evidence that the agent behaves as intended in a structured set of scenarios. Combined with ongoing monitoring in production, it creates a reasonable assurance framework for agent deployments.

For agents that produce outputs that humans can evaluate (summaries, reports, recommendations), periodic auditing of those outputs provides a continuous alignment check. A random sample of outputs is reviewed by human evaluators who assess whether the output is consistent with what was intended. Systematic deviations, such as outputs that consistently favor certain conclusions or omit certain types of information, are signals of alignment failures that automated monitoring might miss.

Output auditing is especially important for agents in high-stakes domains where alignment failures could have serious consequences, such as agents that assist with medical decisions, financial recommendations, or content moderation. In these domains, the human oversight that auditing provides is often a regulatory requirement rather than an optional safeguard.