What hidden states should an AI agent track when diagnosing CI failures?

Hi, if a CI run fails, there can be multiple explanations, so over the past few days I've been researching what the agent needs to have that helps me diagnose the failing Continuous Integration run. The agent first makes assumptions -----> searches for evidence -----> updates probability of each assumption -------> At the end, the agent takes an action like if very high confidence, then: Ask the user to change the exact thing or to something specific. If medium, then: hold, ask for more search evidence if the value of information is greater, If the low cost of being wrong is too high, then: simply escalate to human I have mapped out some hidden states. Here 'they are; they are not mutually exclusive, as a failing CI can be because of many reasons. H_flaky → Basically, the test/system itself can behave nondeterministically H_fault_revealing → The failure is actually revealing a real bug/regression H_dependency_fault → something is wrong with a dependency H_environment_fault → something in the execution environment is causing the failure H_config_error → some CI/build/runtime configuration is wrong H_shared_root_cause → Multiple failures may actually be coming from the same underlying cause Each hypothesis has a probability that gets updated with evidence. Can you spot any weaknesses in here ? What hidden states did I not include? Are these hidden states actionable? I'd your honest opinion..