Position: A Blueprint for the Reflect-Abstract-Integrate Architecture for Agent Self-Evolution from Production Failures

Production AI agents fail regularly on multi-step tasks, yet most agent frameworks discard failure information after logging an error. We propose the Reflect-Abstract-Integrate (RAI) architecture for parameter-free agent self-evolution from production failures. Reflect performs causal chain extraction on a failed trajectory, identifying the step at which execution diverged from the goal state rather than only recording that the episode failed. Abstract generalizes the resulting causal chain into a transferable conditional avoidance rule by replacing task-specific identifiers with structural type variables, so that a single failure can inform structurally similar tasks in other domains. Integrate maintains the resulting rule set through importance weighting, tracking each rule's measured contribution to failure prevention over its application history, and prunes low-value rules to guard against both rule bloat and degradation of previously successful strategies. RAI requires no parameter updates: the entire self-evolution loop operates through in-context rule injection at planning time. We position RAI against reflect-only methods such as Reflexion and Self-Refine, experience-accumulation methods such as ExpeL and EvolveR, and training-based methods such as ETO, and argue from the structure of the architecture and from gaps identified in the failure-analysis literature that step-level causal grounding combined with importance-weighted integration addresses weaknesses that instance-specific reflection and untargeted experience accumulation do not. This paper presents RAI as a proposed architecture and a mechanistic argument for its design. We have not executed a controlled empirical evaluation, and we describe, as a direction for future work, the evaluation methodology that such a test would require.