Building a Bayesian Agent for Diagnosing CI Failures
Elegant_Quantity_583 · reddit · 2026-08-26
The author is building a Bayesian agent to diagnose root causes of Python CI failures and decide between auto-fixing or escalating to humans.
Core Mechanics:
- Defined 7 mutually exclusive root-cause states (e.g., Source Code Issues, Dependency Failures) with priors based on 567 open-source cases.
- Collects 4 types of evidence (e.g., failed step, changed files, rerun outcome) with likelihood tables calculated using Laplace smoothing.
- Uses a greedy EIG strategy to query evidence sequentially, updating beliefs dynamically.
- Implements an action policy: triggers code or dependency fixes when specific state probabilities exceed thresholds, otherwise escalates.
Seeking Feedback:
The author shared specific probability tables and synthetic test cases, asking the community to review the validity of the probability assumptions and the realism of the test scenarios.
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