Efficient multi-provider agent environments with AI gateways: best practices
Organizations are increasingly using multiple models to build AI agents in order to find the best balance of performance and cost for each agentic task and LLM call. As we…
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Datadog achieves GovRAMP High authorization
As state and local governments modernize critical technology systems, they must also meet growing demand for cybersecurity, reliability, operational efficiency, and fiscal accountability. From citizen services and public safety operations…
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How we brought agentic workflows to Cloud SIEM with the Datadog MCP Server
Security engineers using Cloud SIEM spend their day-to-day investigating signals, tuning detection rules, managing suppressions, running historical jobs across interconnected workflows, and more. Agents are becoming a practical way to…
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Protect AWS Strands Agents with Datadog AI Guard
AI agents can reason through tasks, call tools, and adapt their next steps based on intermediate results. That flexibility is useful for building agentic applications, but it also creates security…
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Making agentic token costs visible in production
In some organizations, high token counts have become a proxy for productivity. Some engineering teams are being pushed to max out context windows and wire in sprawling tool sets. More…
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Debug and evaluate your AI app from your coding agent with Datadog Agent Observability
Coding agents like Claude Code, Cursor, and Codex CLI handle the coding parts of building an AI application well. The harder work comes after: understanding why a response went wrong,…
