An operations engineer reports that a Claude-based pipeline began returning malformed JSON responses after a...

The strongest investigation combines configuration comparison with controlled replay. Comparing the current system prompt and output-schema settings against the last known good version identifies maintenance-related configuration drift. Relevant differences include removed formatting instructions, altered schema requirements, incompatible parser expectations, changed stop sequences, or a disabled structured-output mechanism.

Replaying known premaintenance requests against the current configuration creates a controlled test set. Inspecting the raw response before parsing then establishes whether malformed JSON originates in model generation or is introduced by streaming assembly, transport handling, post-processing, or the downstream parser. If the raw response is valid but the pipeline rejects it, the fault lies after generation. If the raw response is malformed, the prompt, schema enforcement, stop reason, and request configuration become the primary investigation targets.

Clearing the prompt cache assumes cache corruption without evidence. Changing model tiers introduces another variable and weakens causal attribution. Raising `max_tokens` is appropriate only when raw responses show truncation and the returned stop reason indicates the token limit was reached; it is not the most direct initial test.

Study Guide references/topics: [Increasing output consistency](https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/increase-consistency); [agent evaluation methodology] (https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents); regression replay; last-known-good comparison; raw-output inspection.

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