Why Red Team AI Agents?

Modern AI agents go beyond single-turn LLM queries. They plan, act, and interact with tools, environments, and users—making them significantly more powerful but also more vulnerable. Red teaming AI agents is essential to identify:

• Unsafe or unaligned behavior over time

• Tool misuse or overreach

• Decision loops, hallucinations, and manipulation

• Failures in reasoning or goal optimization

What Does Detoxio Offer for Agent Red Teaming?

Detoxio AI extends its red teaming engine beyond simple prompts—enabling evaluation of interactive, tool-using agents across multiple steps and scenarios.

Key Capabilities:

• Interactive Evaluation
  Red team multi-turn agents, simulated personas, and action-based decision flows.

• Agent Providers
  Support for local agent frameworks (e.g., LangGraph), HTTP/Gradio-based tools, and LangChain prompt templates (via LangHub plugin).

• Tactics for Agent Testing
  Apply specialized tactics like:

  • Goal misalignment tests

  • Chain-of-Thought derailments

  • Tool abuse prompts

  • Looping or confusion triggers

• Prompt + Tool Evaluation
  Combine natural language prompts with simulated tool output, measuring how agents handle complex tasks.

• Custom Agents + Custom Data
  Test agents using custom plans, datasets, and real-world use cases like document QA, browsing, or autonomous code generation.

  
  

Red Teaming Architecture for Agents

Detoxio models the full agent interaction loop and helps track decision points and risk vectors.

Example Use Cases

• Evaluate LangChain / LangGraph Agents

• Test Agents with Tool Access (e.g., search, calculator)

• Stress-test multi-step reasoning with ambiguity and noise

• Analyze alignment decay in multi-turn dialogs

• Audit decision transparency in AI agents

Getting Started

Use the LangHub plugin to test popular LangChain agent templates:

Then select LangHub Prompts and choose from available templates like rlm/rag-prompt.