LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications
Summary: arXiv:2603.27355v1 Announce Type: new
Abstract
We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow. The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract, then aggregates workflow success, policy compliance, groundedness, retrieval hit rate, cost, and p95 latency into scenario-weighted readiness scores with Pareto frontiers.
We evaluate the harness on ticket-routing workflows and BEIR grounding tasks (SciFact and FiQA) with full Azure matrix coverage (162/162 valid cells across datasets, scenarios, retrieval depths, seeds, and models). Results show that readiness is not a single metric: on FiQA under sla-first at k=5, gpt-4.1-mini leads in readiness and faithfulness, while gpt-5.2 pays a substantial latency cost; on SciFact, models are closer in quality but still separable operationally. Ticket-routing regression gates consistently reject unsafe prompt variants, demonstrating that the harness can block risky releases instead of merely reporting offline scores. The result is a reproducible, operationally grounded framework for deciding whether an LLM or RAG system is ready to ship.
Key Features of the Readiness Harness
- Automated Benchmarks: The harness integrates automated benchmarks to streamline the evaluation process, reducing the time required to assess model readiness.
- OpenTelemetry Observability: By utilizing OpenTelemetry, the system provides comprehensive observability, allowing developers to monitor key metrics and understand model behavior in real-time.
- CI Quality Gates: Continuous Integration (CI) quality gates ensure that only models meeting established criteria are approved for deployment, minimizing the risk of introducing faulty systems into production.
- Minimal API Contract: The system adheres to a minimal API contract, simplifying integration and usage for developers.
- Aggregated Readiness Scores: The harness aggregates multiple factors, including workflow success, policy compliance, and latency, to produce scenario-weighted readiness scores, facilitating informed decision-making.
Evaluation and Results
The evaluation of the readiness harness utilized various datasets and scenarios, providing insights into the operational effectiveness of different models. The results indicate that:
- On the FiQA task, the gpt-4.1-mini model demonstrated superior readiness and faithfulness compared to its counterparts.
- The gpt-5.2 model, while advanced, incurred a significant latency cost, highlighting the trade-offs involved in model selection.
- In the SciFact task, while models exhibited comparable quality, operational differences remained, underscoring the importance of evaluating real-world performance.
Conclusion
The LLM Readiness Harness represents a significant advancement in the deployment of LLM and RAG applications. By transforming evaluation into a deployment decision workflow, the harness not only enhances model readiness assessment but also actively mitigates risks associated with deploying unreliable models. The findings suggest that organizations can leverage this framework to make more informed decisions about model deployment, ultimately leading to safer and more reliable AI applications.