Expose operational telemetry and SLO gates for override cascade dspy (dspy framework detecting preventing safety)

[haasonsaas]

Summary

Define the observable contract for latency, cost, correctness, and degraded-mode behavior.

This issue was generated from an org-wide EvalOps mining pass on 2026-05-10 07:57 UTC. It combines live GitHub repo signals with a per-repo arXiv search. Treat the research links as grounding for a concrete implementation, not as a request for a literature review.

Repo Evidence

• Repository description: DSPy framework for detecting and preventing safety override cascades in LLM systems. Research-grade implementation for studying when completion urgency overrides safety constraints.
• Tree signals: 7 docs files, 0 workflows, 0 proto files, 5 test-like files.
• README.md:112 includes latent-spec language: ```bash # Run comprehensive evaluation python -m override_cascade_dspy.override_cascade.main --demo
• README.md:120 includes latent-spec language: # Run multi-provider threshold evaluation python evaluations/multi_provider_override_evaluation.py
• README.md:172 includes latent-spec language: ### Multi-Provider Evaluation Results
• README.md:174 includes latent-spec language: Our comprehensive evaluation across extreme override scenarios demonstrates consistent safety override patterns:
• README.md:429 includes latent-spec language: ### Evaluation Protocol
• README.md:438 includes latent-spec language: ### Evaluation Metrics

Research Grounding

Repo axes: research, evaluation, tooling, security

Search keywords: override, safety, cascade, evaluation, pressure, urgency, pattern, research, https, step, when, framework

• arXiv:2604.04869v1 Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning (Shiek Ruksana, Sailesh Kiran Kurra, Thipparthi Sanjay Baradwaj), 2026.
• arXiv:2507.03620v1 Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy (Francisca Lemos, Victor Alves, Filipa Ferraz), 2025.
• arXiv:2503.11118v1 UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning (Kristin Qi, Youxiang Zhu, Xiaohui Liang), 2025.
• arXiv:2412.15298v1 A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation (Bhaskarjit Sarmah, Kriti Dutta, Anna Grigoryan, Sachin Tiwari, Stefano Pasquali, Dhagash Mehta), 2024.
• arXiv:2506.19773v2 Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study (Nandana Mihindukulasooriya, Niharika S. D'Souza, Faisal Chowdhury, Horst Samulowitz), 2025.
• arXiv:2503.23803v2 Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute (Yingwei Ma, Yongbin Li, Yihong Dong, Xue Jiang, Rongyu Cao, Jue Chen), 2025.
• arXiv:2505.05541v1 Safety by Measurement: A Systematic Literature Review of AI Safety Evaluation Methods (Markov Grey, Charbel-Raphaël Segerie), 2025.
• arXiv:2602.03411v2 SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training (Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le, Daixuan Cheng), 2026.
• arXiv:2602.03419v1 SWE-World: Building Software Engineering Agents in Docker-Free Environments (Shuang Sun, Huatong Song, Lisheng Huang, Jinhao Jiang, Ran Le, Zhihao Lv), 2026.
• arXiv:2602.13757v2 Assessing the Case for Africa-Centric AI Safety Evaluations (Gathoni Ireri, Cecil Abungu, Jean Cheptumo, Sienka Dounia, Mark Gitau, Stephanie Kasaon), 2026.

What To Build

• Name the key service/user journey SLOs and their required dimensions.
• Emit metrics/log fields for success, failure, cost/latency, and reasoned fallback.
• Add a dashboard/runbook stub or CLI report that makes the new signals operator-visible.

Acceptance Criteria

• A short design note names the repo-specific workflow, threat or correctness model, and the research assumptions being adopted.
• A runnable check, fixture, or verifier exercises the new contract in CI or an equivalent local command documented in the repo.
• The implementation emits or stores enough evidence for a downstream agent/operator to cite inputs, decisions, and outputs.
• At least one negative/degraded-mode case is covered so failures are observable rather than silently accepted.
• Documentation links the new behavior to the relevant EvalOps platform primitive or explicitly records why this repo remains standalone.

Notes

• Generated issue 4/5 for evalops/override-cascade-dspy by evalops_org_miner.py.
• Before implementation, confirm the sampled latent-spec snippets still match main; this issue intentionally cites exact file paths/lines where the mining pass saw them.