Splunk Blogs

This field guide categorizes AI agents across seven levels of autonomy, providing a roadmap for mapping system architectures against common failure modes and production-ready requirements.

Measure and improve AI accuracy by distinguishing between classification metrics, generative faithfulness, and agentic trajectory completion.

Learn how to validate AI models across the full lifecycle. From development evals to runtime guardrails, close the gap between benchmarks and production behavior.

Enterprise AI requires core components—including data pipelines, security, and observability—to successfully move agentic applications from demo to production.

Understand context engineering as a systematic discipline for curating agent inputs to improve reliability, reduce costs, and optimize performance.

Build a deep research agent with plan-and-execute in LangGraph, GPT-5.6, and Tavily. Plus span-level evals that catch planning and retrieval failures.

Announcing the revised Event Agentic SOC Reference Architecture & Operations Guide.

We’re helping teams evaluate agent behavior, observe performance, optimize costs, and block harmful actions.

Observability Essentials and Observability Premier simplify how customers buy and expand observability.