# Agent Red Teaming Highflame RedTeam provides automated agent red-teaming capabilities to identify security, safety, and reliability risks in AI agents operating in real-world environments. Rather than testing models in isolation, agent red teaming evaluates the entire AI system, including prompts, orchestration logic, tool usage, external APIs, and decision flows, to uncover vulnerabilities that emerge from how these components interact. Many of the most critical AI failures do not stem from the underlying language model alone, but from the way agents are composed, granted permissions, and allowed to act autonomously. Agent red teaming addresses this gap by simulating realistic adversarial behavior, stress-testing agent workflows end-to-end, and exposing failure modes that are difficult to detect through unit tests or manual review. Highflame RedTeam is built as a modular, agent-driven security testing platform that continuously probes live or staging agents using research-backed attack techniques. By combining automated attack generation, distributed execution, and structured evaluation, it enables organizations to proactively identify hidden vulnerabilities, validate safeguards, and operate agentic systems with greater confidence. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.highflame.ai/red-teaming/agent-red-teaming.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.