Where do most AI debugging tools break down? and why? (resolve.ai)

Resolve AI argued that the real bottleneck in modern engineering isn’t AI’s ability to generate code quickly, but the architecture around how we use AI to operate production systems. At a recent Stanford presentation they promoted “AI-native engineering,” where persistent, stateful multi-agent systems (not single-shot LLMs or tool-augmented chatbots) become the primary interface for incident response and operations. Instead of humans manually correlating metrics, logs, traces and deploy history, AI agents triage, run parallel hypotheses, and synthesize findings from multiple specialized subsystems—turning “analyze these logs” into “resolve this checkout failure.” Technically, the post contrasts four approaches: LLMs (one-off tasks), LLM+tools (fetch data but no persistent context), single agents (sequential, bottlenecked reasoning), and multi-agent systems (parallel, specialized agents coordinated by formal protocols). The key limitations are lack of persistent state, limited context windows, and single-agent sequential reasoning—problems multi-agent architectures aim to solve through stateful coordination, conflict resolution schemas, and domain-specialized agents. The implication for AI/ML teams: building production-ready agentic SREs requires deep expertise in both systems engineering and AI orchestration. Resolve AI claims to combine this domain knowledge and lists customers benefiting from higher velocity and reliability, highlighting a shift in engineer roles from tactical investigation to high-level architectural work.