Datadog Agent Observability
Product · Developer tool by Datadog
Ship AI agents faster and with confidence through unified evaluation and production tracing.
An AI-powered observability platform for developers to evaluate, trace, and improve AI agents from development to production. Choose it to correlate agent reasoning with full-stack performance and optimize model quality using real production data.
- Languages
- English
Category: Observability & Monitoring
- Deployment
- Cloud-based platform with agent-based data collection
- Tracing Technology
- Datadog Tracer
- Data Retention
- 15 months for high-resolution metrics
- Evaluation Modes
- Automated evaluators and human review
- Integration Support
- Leading models and agent frameworks
- Monitoring Granularity
- Step-by-step execution context
- Trace every step from prompts to tool calls and agent decisions
- Compare prompts, models, and configurations side-by-side
- Build versioned golden datasets from real production traces
- Monitor token usage, latency, and errors at every execution step
Overview
Datadog Agent Observability is a specialized monitoring and evaluation platform designed for developers and AI engineers building agentic systems. It provides end-to-end visibility into AI agent workflows by tracing every step from initial prompts and retrieval to tool calls and final responses. The tool enables teams to move from prototype to production by offering offline experimentation, production monitoring, and the ability to build versioned golden datasets from real interactions. Users can compare prompts and models side-by-side to optimize for cost, quality, and latency. By correlating agent behavior with backend infrastructure and user experience data, it helps pinpoint failures across the entire application stack.
Monitoring and Debugging
- Step-by-Step Tracing: The platform follows every request across prompts, retrieval steps, tool calls, and agent decisions to see exactly how a system reached each response.
- Usage & Latency Tracking: Users can monitor latency, token usage, retries, and errors at every step to spot slow or heavy calls without guessing.
Evaluation and ExperimentationOps
- Production-Based Datasets: The platform turns real production traces into versioned datasets that can be tested against to validate changes on real behavior.
- Human Feedback & Annotation: Teams can use human review and annotation to label and grade outputs at scale where automated checks fall short.
- Automated Evaluation: Users can measure model and agent quality over time using built-in evaluators or custom ones tied to specific KPIs.
Governance and Safety
- Safety & Risk Detection: The system identifies hallucinations, prompt injection attempts, and PII exposure as they happen to track quality trends.
- Governance Controls: Teams can confidently run AI in production with precise alerting and role-based access control to reduce risk.
- Broad Framework Support: The platform supports leading models, frameworks, and agent frameworks to move from prototype to production faster.
Use cases
- AI engineers trace agent reasoning steps to identify why a specific tool was called or why a response failed.
- Developers compare different LLM models and prompt versions against production-based datasets to improve accuracy.
- SRE teams monitor token usage and latency across AI agents to manage costs and ensure system reliability.
- Product teams use human annotation tools to grade AI agent outputs and build high-quality training data.
Specifications
- Integrations
- Leading models, frameworks, and agent frameworks; APM; RUM; Infrastructure Monitoring
- Deployment
- Cloud-based platform with agent-based data collection
- Tracing Technology
- Datadog Tracer
- Data Retention
- 15 months for high-resolution metrics
- Evaluation Modes
- Automated evaluators and human review
- Integration Support
- Leading models and agent frameworks
- Monitoring Granularity
- Step-by-step execution context
How it works
- Instrument the Application: Deploy the Datadog tracer within the application environment to start collecting telemetry from AI agents and LLM calls.
- Monitor Production Behavior: View execution graphs and traces in the Datadog platform to understand agent reasoning, tool usage, and performance.
- Evaluate and Iterate: Capture production traces to create versioned datasets and run experiments to compare different prompts or model versions.
FAQ
Can I use production data for testing AI agents?
Yes, the platform can turn real production traces into versioned golden datasets to test prompt, model, and tool changes against real behavior before rollout.
How does the platform evaluate AI agent quality?
Datadog provides built-in and custom evaluators to measure quality, cost, and latency across releases, supplemented by human review and annotation tools.
What usage metrics are tracked for AI agents?
The platform monitors latency, token usage, retries, and errors at every step of the agent's execution path.
Added by AntSpace from public sources (datadoghq.com).