Traditional observability was built to answer "what broke?" in deterministic software. AI agents are non-deterministic, so the same request rarely follows the same path twice. That shift created a trust gap: teams can see that an agent ran, but not whether it was right, safe, or worth the cost. Splunk frames the answer as four pillars of agentic observability.
Trace a request end to end, from the agent and model to vector databases, model-serving endpoints, and the GPUs underneath. Correlating these signals in one view means you find the root cause of a failure in minutes, instead of learning about it from user complaints.
Score outputs for hallucination, bias, toxicity, and relevance on live traffic, not just a sample. Splunk notes that an untuned model acting as a judge reaches only about 70 percent accuracy, so roughly one in three scores can be wrong. Purpose-built small language models make accurate evaluation affordable at production scale.
Splunk reports that a single agent interaction can consume tens of thousands of tokens. Tokenomics attributes that spend to specific agents, workflows, and models, and pairs it with output quality, so you can tell which agents return real value and which simply run up the bill.
Turn trusted evaluations into runtime guardrails that stop prompt injection, data leakage, and tool misuse before an agent action reaches the user. The result is agents that stay within policy while they run in production. bitsIO sets up these guardrails as part of its AI Agent Security work.
As agents scale, token spend can climb faster than the value they return. Finance sees the invoice while engineering sees the sprint activity, and connecting the two is hard. Agentic observability attributes token spend to specific agents and workflows, so both teams work from the same numbers and wasteful spend is caught early.
bitsIO is a 5X Splunk Partner of the Year and Splunk Trusted Partner with 300+ enterprise clients. Our Splunk professional services team helps enterprises move agentic observability from a pilot to production, then operate it day to day.
bitsIO reviews how your agents run today, where observability gaps sit, and which signals matter most for your use case, then maps a practical path to production.
We instrument your agents and models and connect them to the data you already collect in Splunk, Splunk Observability, cloud data lakes, and more, so agent behavior shows up alongside your existing telemetry.
bitsIO brings agent behavior, token cost, and infrastructure health into one correlated view, so a problem in one shows up right next to its cause in another. Instead of checking agent logs, cost reports, and infrastructure metrics in separate tools, your team works from a single picture and can act on it faster.
We set up evaluations for the quality and safety signals that matter to you, promote the trusted ones into runtime guardrails, and help your team tune them and right-size models against cost as your AI footprint grows.
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Agentic observability isthe practice of monitoring AI agents, and the models and infrastructure behindthem, in production. It tracks four things: performance, output quality, tokencost, and safety. The goal is to see whether agents work as intended, what theycost, and where they create risk
Traditional monitoring tracks uptime, latency, and errors in deterministic software. AI agents are non-deterministic, so the same request can take a different path each time. Agentic observability adds output quality, hallucination, safety checks, and token cost to the usual signals, and correlates them across the whole AI stack.
Tokenomics is the tracking of how many tokens your agents consume and what that spend delivers. Splunk reports that a single agent interaction can run to tens of thousands of tokens. Without visibility, cost climbs without matching value. Tracking token use by agent and workflow ties AI spend to business outcomes.
Guardrails are runtime controls that stop unsafe or inaccurate agent actions before they reach the user, such as prompt injection, data leakage, and tool misuse. Observability has to come first, because you cannot block what you cannot see. bitsIO sets up these guardrails as part of its AI Agent Security work
bitsIO is a five-time Splunk Partner of the Year and Splunk Trusted Partner. Our professional services team helps you implement agentic observability on Splunk: evaluate agent behavior, trace workflows down to the infrastructure, track token cost, and apply guardrails across production traffic.
A typical engagement covers an assessment of your AI stack, implementation on Splunk, correlation of agent behavior with cost and infrastructure, and setup of evaluations and runtime guardrails. bitsIO then helps your team operate and tune it over time.
A bitsIO engineer will review how your agents run today and map a path to full-stack agentic observability on Splunk.
We're a team of passionate experts helping businesses like yours extract maximum value from their data investments. Whether you need to streamline data pipelines, build AI-powered solutions, or unlock advanced analytics, we have the expertise to guide you through every step.
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