Datadog announced new agentic AI monitoring and experimentation capabilities to give organizations end-to-end visibility, rigorous testing capabilities, and centralized governance of both in-house and third-party AI agents.
The new capabilities include AI Agent Monitoring, LLM Experiments and AI Agents Console.
Datadog is bringing observability best practices to the AI stack. Part of Datadog’s LLM Observability product, these new capabilities allow companies to monitor agentic systems, run structured LLM experiments, and evaluate usage patterns and the impact of both custom and third-party agents. This enables teams to deploy quickly and safely, accelerate iteration and improvements to their LLM applications, and prove impact.
“A recent study found only 25 percent of AI initiatives are currently delivering on their promised ROI—a troubling stat given the sheer volume of AI projects companies are pursuing globally,” said Yrieix Garnier, VP of Product at Datadog. “Today’s launches aim to help improve that number by providing accountability for companies pushing huge budgets toward AI projects. The addition of AI Agent Monitoring, LLM Experiments and AI Agents Console to our LLM Observability suite gives our customers the tools to understand, optimize and scale their AI investments.”
Now generally available, Datadog’s AI Agent Monitoring instantly maps each agent’s decision path–inputs, tool invocations, calls to other agents and outputs–in an interactive graph. Engineers can drill down into latency spikes, incorrect tool calls or unexpected behaviors like infinite agent loops, and correlate them with quality, security and cost metrics. This simplifies the debugging of complex, distributed and non-deterministic agent systems, resulting in optimized performance.
In preview, Datadog launched LLM Experiments to test and validate the impact of prompt changes, model swaps or application changes on the performance of LLM applications. The tool works by running and comparing experiments against datasets created from real production traces (input/output pairs) or uploaded by customers. This allows users to quantify improvements in response accuracy, throughput and cost—and guard against regressions.
Datadog unveiled AI Agents Console in preview, which allows organizations to establish and maintain visibility into in-house and third-party agent behavior, measure agent usage, impact and ROI, and proactively check for security and compliance risks.
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