Software debugging traditionally involved examining logs, payloads, traces, databases, and code versions. This process relied on the deterministic nature of code, where identical inputs and states yielded identical results. However, this axiom is eroding as systems shift towards stochastic execution windows, employing agents that interpret context, use tools, and retrieve dynamic data. These agents can autonomously make high-consequence decisions like triggering refunds or bypassing payment retries.When these agentic decisions are flawed, explaining them becomes incredibly difficult because current systems lack a clear audit trail for such autonomous judgments. Traditional enterprise systems provide a verifiable chain of custody for programmatic flows, allowing for systematic root cause analysis. However, autonomous agents introduce a new, complex topology involving prompts, context retrieval, model inference, and tool selection, transforming the execution path entirely.Logging the system prompt and LLM completion is insufficient for an audit trail, as it only captures a snapshot of a single moment. True understanding requires reconstructing the entire decision environment, including model lineage, context window state, retrieval provenance, and tool availability. Traditional observability outputs, like execution logs, show what API calls were made but not the justification behind the agent's judgment.Engineering for autonomous agents in critical pathways necessitates treating decision provenance as a core architectural requirement. This involves outputting a structured, immutable manifest of the facts and evidence available to the agent at the time of execution rather than dumping internal reasoning. Classifying agent decisions by consequence – low, medium, and high – allows for appropriately scaled decision provenance requirements.The industry is shifting from asking "Can we build an agent?" to "Can you prove why your agent did that?" Future compliance demands will require demonstrable justification for autonomous actions, similar to how human code changes are audited. The next generation of reliable software architecture will be defined by the transparency and auditability of its autonomous agents.