See how AI agents actually work.
Watch the trace unfold.

The foundation. The LLM calls a tool to get real data instead of hallucinating. One question, one tool call, one answer.

The agent thinks step by step. It fetches data, reasons about it, fetches more if needed, and builds up to an answer. You see the reasoning chain.

The agent writes and executes Python code on the fly. No pre-built tool needed — it generates the calculation dynamically.

The full picture. The agent plans the analysis, fetches data with Tool Calling, reasons with ReAct, calculates with CodeAct, and synthesizes a recommendation.

The LLM queries live infrastructure data — service metrics, dependency health, deployed resources. One question, one tool call, one answer.

The agent diagnoses issues and evaluates compliance by pulling metrics from multiple sources, reasoning about what it finds, and iterating until it has the full picture.

The agent writes Python to calculate infrastructure metrics — capacity planning, cost estimation, availability SLAs. No pre-built calculator needed.

All patterns combined. The agent plans the investigation, queries infrastructure and compliance data, computes risk scores, and synthesizes a full report.