What you are probably seeing
You calculate the hours saved by automated tasks, but your ticket queue for edge cases grows at the same rate, often requiring human intervention at odd hours.
When you read your own documentation, the distinction between the system’s intended behavior and its actual execution logic is blurred, forcing you to guess which bot is responsible for specific failures.
Why it happens
Teams focus on the initial deployment of automation tools without designing the downstream accountability structure needed to handle anomalies and drift.
Documentation often describes the happy path of execution rather than the intent behind decision points, leaving judge agents and human reviewers without clear criteria for validating outcomes.
What changed recently
Step by step
1. Map every current automation step to a specific human owner who is responsible for reviewing exceptions and drift.
2. Rewrite your plan file to explicitly separate the intent of each workflow from the technical execution details.
3. Identify the smallest friction point in your current maintenance queue and document the decision criteria used to resolve it.
4. Test a new agent task against a judge agent to verify that the system can distinguish between a valid result and an error without human input.
5. Stop adding new bots until you can clearly explain how the next exception will be handled and who is accountable for the review.
Ways to go further
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