Red-Teaming Agentic AI Workflows: A Practical Starting Point

Most AI red-teaming practice was built for single-turn, single-model interactions: send an adversarial prompt, check the response. Agentic workflows break that model because the risk isn't only in what the model says—it's in what it does with the tools, memory and state it has access to across multiple steps.

A model can pass every isolated safety check and still produce an unsafe outcome once it's given the ability to call APIs, write files, or trigger downstream actions in sequence.

Effective agent red-teaming probes the boundaries of the whole system: can a malicious or malformed input get the agent to invoke a tool outside its intended scope? Does the agent correctly refuse a task it lacks permission for, or does it find a workaround? What happens when tool output itself is adversarial—can a poisoned search result or document manipulate the agent's next action?

This means building test scenarios around multi-step task chains, not just prompts, and instrumenting the agent's tool calls and intermediate state so failures are visible, not just the final output.

Our Quality Engineering service includes red-teaming built specifically for agentic and tool-using AI workflows, tested against real-world conditions before you ship—not just the underlying model in isolation.