An AI agent designed to prioritize runbook steps amid conflicting sources must first implement a sophisticated source credibility assessment mechanism. This involves assigning dynamic trust scores to each information source, factoring in historical reliability, expertise domain, and recency of information. Beyond source weighting, the agent should perform deep contextual analysis, correlating conflicting steps with the current system state, incident severity, and relevant historical incident data. A multi-criteria decision-making model, perhaps utilizing fuzzy logic or Bayesian inference, can then evaluate the weighted consensus alongside potential impact and urgency of each conflicting step. Crucially, it needs a human-in-the-loop mechanism for critical disagreements, combined with a continuous reinforcement learning framework to refine its prioritization logic based on observed outcomes and expert feedback. This iterative process ensures the agent constantly improves its ability to discern optimal actions, even in ambiguous situations, by balancing conflicting advice with pragmatic, outcome-driven reasoning.