Here is a rewritten version of the text in a more natural and human-like tone:
**Building a Self-Testing Agentic AI System to Red-Team Tool-Using Agents and Enforce Safety at Runtime**
In this tutorial, we’ll create a sophisticated red-team analysis harness using Strands Agents to stress-test a tool-using AI system against prompt-injection and tool-misuse attacks. We’ll handle agent security as a first-class engineering problem by orchestrating multiple brokers that generate adversarial prompts, execute them against a guarded goal agent, and evaluate the responses with structured analysis standards.
By working everything in a Colab workflow and using an OpenAI model through Strands, we’ll show how agentic techniques can be used to gauge, supervise, and harden other agents in a practical, measurable manner.
**The Full Codes**
You can check out the full codes [here](https://github.com/Marktechpost/AI-Tutorial-Codes-Included/blob/main/Agentic%20AI%20Codes/strands_agentic_red_teaming_tool_injection_harness_Marktechpost.ipynb).
**Setting Up the Runtime Environment**
We’ll set up the runtime environment and install the required dependencies to ensure the system runs smoothly. We’ll securely retrieve the OpenAI API key and initialize the Strands OpenAI model with carefully chosen generation parameters, guaranteeing consistent behavior across all agents.
**Defining the Goal Agent**
We’ll define the goal agent, along with a set of mock instruments that simulate sensitive capabilities such as secret access, file writes, outbound communication, and computation. We’ll implement strict behavioral constraints by the system prompt, ensuring the agent refuses unsafe requests and avoids misuse of instruments.
**Generating Adversarial Prompts**
We’ll create a dedicated red-team agent designed specifically to generate adversarial prompt-injection attacks. We’ll instruct it to use various manipulation methods such as authority, urgency, and role-play to emphasize the goal agent’s defenses. This automated attack generation ensures broad coverage of realistic failure modes without relying on manually crafted prompts.
**Structuring the Attack Results**
We’ll introduce structured schemas for capturing security outcomes and a judge agent that evaluates responses. We’ll formalize analysis dimensions such as secret leakage, tool-based exfiltration, and refusal quality, transforming subjective judgments into measurable indicators. By doing this, we make security analysis repeatable and scalable.
**Running the Target Agent with Observation**
We’ll execute every adversarial prompt against the goal agent while wrapping each instrument to report how it’s used. We’ll capture both the natural language response and the sequence of instrument calls, enabling exact inspection of agent behavior under stress.
**Building the Red-Team Report**
We’ll orchestrate the total red-team workflow from attack generation to reporting. We’ll mix individual evaluations into abstract metrics, identify high-risk failures, and surface patterns that suggest systemic weaknesses.
**Conclusion**
In conclusion, we’ve built a fully working agent-against-agent security framework that goes beyond simple prompt testing and into systematic, repeatable analysis. We’ve shown how to observe instrument calls, detect secret leakage, rate refusal quality, and combine outcomes into a structured red-team report that can inform real design decisions. This approach permits us to constantly probe agent behavior as instruments, prompts, and models evolve, and it highlights how agentic AI is not just about autonomy, but about building self-monitoring systems that stay secure, auditable, and strong under adversarial stress.
**Takeaway**
Check out the full codes [here](https://github.com/Marktechpost/AI-Tutorial-Codes-Included/blob/main/Agentic%20AI%20Codes/strands_agentic_red_teaming_tool_injection_harness_Marktechpost.ipynb). Additionally, feel free to follow us on [Twitter](https://x.com/intent/follow?screen_name=marktechpost) and don’t forget to join our [100k+ ML SubReddit](https://www.reddit.com/r/machinelearningnews/) and Subscribe to our [Newsletter](https://www.aidevsignals.com/).
