Inside Splunk Agent Observability - Building Reliable, Cost-Effective AI Agents
Scaling AI agents remains a complex undertaking. The "black box" effect obscures the correlation between operational performance and bottom-line outcomes, introducing non-deterministic behaviors, hidden biases, and runaway token loops that inflate cloud bills.
Join the Splunk AI & Observability engineering team for a live technical session on how Splunk Agent Observability (powered by Galileo) enables practitioners to evaluate, observe, and protect AI agents from development through production.
Key Takeaways:
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Building stronger AI guardrails — how to address unpredictable agent behavior, inaccurate outputs, and governance challenges as AI moves into production.
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Improving AI reliability without sacrificing speed or cost — including the challenges around evaluation accuracy, time pressure, latency, and the cost of running AI evaluations at scale.
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Using Agent Observability in practice — a look at evaluating agent and RAG quality, identifying the root cause of failures across multi-agent workflows, and applying runtime guardrails to help prevent hallucinations, prompt injection, and unsafe outputs.
Space is limited, don't miss out!
