What is Agentic Red Teaming?

Agentic red teaming is a specialized testing capability on our platform that uncovers vulnerabilities in AI agents — systems that go beyond simple question and answer interactions to make decisions, use tools, access memory, and work with other agents across multi step workflows. Traditional red teaming tests how a model responds to prompts. Agentic red teaming tests what happens when that model is given autonomy to act.

Why it matters

When a large language model operates as an agent, it does more than generate text. It plans tasks, calls APIs, retrieves data from external sources, delegates to other agents, and loops through decisions until it reaches a goal. Each of those steps is a potential point of failure that would never show up in a standard model test.

Our research with University College London found a 67% exploit success rate when testing models inside an agentic loop, compared to 0% when the same model was tested on its own. The vulnerabilities were not in the model. They were in the orchestration layer — how agents coordinate, pass data, and make sequential decisions together.

Our agentic red teaming is built on the AgentSeer framework, developed in collaboration with University College London (UCL). AgentSeer converts execution logs into interactive knowledge graphs so you can see exactly what happened inside a multi agent system.

The platform breaks this down into two types of graph based analysis:

Agent graphs show the full sequence of what an agent did. Every decision, every tool it called, every piece of data it accessed. Component graphs show the bigger picture — how different agents, tools, and memory systems relate to each other inside a multi agent setup.

When you put both together, you can trace exactly where in a complex workflow something went wrong and why.

What the platform captures

Each graph is made up of nodes and edges that map the full execution of an agentic system:

Nodes represent the building blocks of your agent workflow:

• Agents - each autonomous agent in the system
• Tasks - the actions and goals agents are working on
• Tools - APIs, databases, and external services agents call
• Data inputs and outputs - what goes in and what comes out at each step
• Humans - any human in the loop interactions or interventions

Edges capture the relationships between them:

• Tasks delegated or sequenced between agents
• Tools required and used at each step
• Inputs consumed and outputs produced
• Interventions from humans or other agents

Every element in the graph links back to its exact trace span, so nothing is abstracted away. You can click into any node or edge and see the raw execution data behind it.

What we test for

The platform runs adversarial assessments across the full agent workflow, including:

• Adversarial prompts that target agent reasoning and planning
• Trust building exploitation where prompts gradually escalate to bypass safety controls
• Multi turn manipulation that tests how agents behave over extended interactions
• Tool misuse scenarios where agents are pushed to call tools in unintended ways
• Deception and exfiltration detection across the agent's data access path

Results include perturbation testing (introducing controlled changes to see how the system reacts) and causal attribution (identifying which specific component caused a failure). This goes beyond pass/fail — it tells you exactly what broke and why.

This research was recognized when our team won a Top 10 spot in OpenAI's GPT OSS 20B Red Teaming Hackathon, earning a $50K award.

If you want to know more about how we do agentic red teaming and agent graph analysis, get a demo now.