Datadog Introduces Advanced LLM Observability Tools to Optimize Agentic AI Performance and ROI

Datadog Introduces New Tools for Monitoring and Improving Agentic AI and LLM Performance

Datadog, a leading platform for cloud application monitoring and security, has launched new features to enhance observability for agentic AI and large language models (LLMs). These additions—AI Agent Monitoring, LLM Experiments, and AI Agents Console—offer organizations better visibility, testing, and governance over AI agents, whether built in-house or sourced from third parties.

Bridging the Visibility Gap in AI Systems

As AI agents become integral to software products and workflows, companies often struggle to track their behavior and impact. Datadog’s new capabilities, part of their LLM Observability suite, address this by providing end-to-end transparency into agent decision paths, tool usage, and outputs. This helps teams detect unexpected behavior, troubleshoot issues, and measure real business value.

AI Agent Monitoring visualizes each agent’s decision-making process using an interactive graph. Engineers can identify latency problems, incorrect tool calls, or infinite loops and correlate these issues with performance, security, and cost metrics. This makes debugging complex, distributed AI systems more manageable and speeds up optimization.

Validating AI Changes with LLM Experiments

Datadog’s LLM Experiments tool allows developers to rigorously test prompt modifications, model swaps, or application updates. By running experiments on datasets derived from real production inputs and outputs, teams can quantify improvements in accuracy, throughput, and cost while guarding against regressions. This structured approach enables faster iteration and safer deployments of LLM-based applications.

Centralized Governance with AI Agents Console

With many organizations embedding third-party agents like OpenAI’s Operator or Salesforce’s Agentforce into workflows, tracking agent usage and permissions becomes critical. The AI Agents Console offers a single view of both internal and external agents, helping teams monitor agent behavior, measure ROI, and proactively identify security or compliance risks.

Industry Perspectives

Timothée Lacroix, Co-founder and CTO of Mistral AI, highlighted the growing importance of observability as AI agents mature beyond chat assistants and gain new tools. He notes that comprehensive monitoring is essential to confidently deploy agentic solutions at scale.

Michael Gerstenhaber, VP of Product at Anthropic, emphasized that as AI agents tackle real-world tasks, observability is key to ensuring safe, valuable, and aligned behavior. He expressed strong support for Datadog’s new features that help scale these systems responsibly.

Why This Matters for IT and Development Teams

• Improved Debugging: Quickly identify and fix issues in complex AI agent workflows.
• Data-Driven Decisions: Validate changes to prompts and models with real-world data.
• Centralized Monitoring: Track usage and risks across multiple AI agents and platforms.
• Faster Iterations: Accelerate development cycles with structured experimentation.

These tools empower development and operations teams to deploy AI responsibly and efficiently, ensuring investments in AI deliver measurable value.

For more information about Datadog’s AI observability solutions, visit their LLM Observability page.