Wrap-up
Congratulations, you have completed the Splunk Agent Observability workshop.
Congratulations, you’ve completed the Splunk Agent Observability workshop!
You took Careful Health Provider’s agentic healthcare assistant from a black box that could quietly tell a patient to take double their dose, and turned it into a system you can see, measure, and govern.
What you accomplished #
- Instrumented the application to trace every agent interaction.
- Traced and investigated agent behavior to find the root cause of errors fast.
- Enabled evaluators to automatically catch hallucinations and tool-selection errors.
- Used Signals to surface the unknown unknowns, the recurring failure patterns you didn’t think to measure.
- Added guardrails to block dangerous actions and steer unsafe answers to safety at runtime.
Why Splunk Agent Observability #
Splunk Agent Observability closes the AI trust gap that traditional infrastructure and APM monitoring can’t see:
- Accurate, low-cost evaluations: purpose-built Luna SLMs detect hallucinations, bias, and more, affordably enough to score all your traffic.
- End-to-end visibility for the entire AI stack: observe agents, models, vector databases, proxies, and more in one place.
- Built-in runtime security and privacy guardrails: block inaccurate and harmful behavior before it ever reaches a customer.
And because it’s part of Splunk Observability, your agent telemetry lives right alongside your infrastructure, APM, and log data: one platform for the whole stack.
Where to go next #
- Add custom Evaluators and Signals tuned to your own failure modes.
- Run Experiments to replace guesswork with evidence and prevent regressions before release.
- Wire experiments into CI/CD as an automated release gate.
- Expand guardrails across more steps: prompt injection, PII exposure, scope enforcement.
- Route different workloads to dedicated agent streams for cleaner separation.
References #
