Give Your AI Pilot a Bad Day

Before expanding an AI agent's role, ask the team to show what happens when the workflow goes wrong. A missing attachment, interrupted tool call, or request outside its authority can reveal more about operational readiness than another clean demonstration.

The goal is a system that handles exceptions predictably enough for people to manage. That means evaluating the agent's actions and the resulting business record, including cases where the correct response is to pause.

Anthropic's January 2026 evaluation guide distinguishes the transcript from the outcome. An agent can say a reservation exists without the underlying system containing one. It also recommends multiple trials because behavior can vary between runs. These distinctions give business owners a useful foundation for a pilot scorecard.

Start with three uncomfortable situations

First, remove something the process needs. For a hypothetical service request, omit a required field or provide two conflicting dates. Define the acceptable response before running the test. The agent might request clarification, retrieve an authorized source, or route the case for review. Inventing the missing detail should not count as completion.

Second, interrupt a dependency. Simulate an unavailable service or a tool response that leaves the outcome uncertain. Check whether the agent distinguishes a failed action from an action that may have completed. Blindly repeating a write could create a duplicate. The recovery path should verify state where possible and provide a useful handoff when uncertainty remains.

Third, cross a permission boundary. Ask for an action the agent is not authorized to take. Assess whether access controls and the workflow prevent execution, and whether the person receiving the escalation understands what approval is missing. Test the controls around the model rather than relying only on instructions inside a prompt.

Keep the scorecard understandable

For each case, record the expected outcome, observed state, boundary violations, recovery behavior, elapsed time, and human work required. Separate partial progress from completed work. Review cases where the grader's verdict disagrees with a domain expert, since an evaluation can also contain errors.

Include ordinary successful cases as well as disruptions. A system that escalates everything may avoid some mistakes while creating an unusable review queue. The business needs to know both how often the agent completes authorized work and how well it recognizes exceptions.

Use the evidence to set a bounded release

Passing every case in a finite test set does not establish universal reliability or grant new authority. The accountable owner should use the evidence, consequences of failure, and available oversight to decide which work can enter a limited rollout.

Keep monitoring after release and add newly observed failures to the test set. The practical question is whether the team can recognize an unsuccessful run, contain its effects, and recover without losing track of the work.

Anthropic: Demystifying evals for AI agents

Stay Connected With Kategos

• Website: Visit kategos.ai to explore the platform and services.
• LinkedIn: Follow Kategos on LinkedIn for industry insights and company updates.
• YouTube: Subscribe to Kategos on YouTube to watch podcast episodes and video content.
• Instagram: Follow @kategos.ai for visual updates and behind-the-scenes content.
• Facebook: Like Kategos on Facebook to stay connected with the community.
• PitchBook: View Kategos on PitchBook for company information and business profile details.
• Spotify: Stream on Spotify.
• Apple Podcasts: Listen on Apple Podcasts.