Agentic AI Red Teaming. Break your Agents before someone else does.

A penetration test probes infrastructure and application code. Agentic red teaming targets the decision layer of an AI system: the prompts, tools, memory, and permissions that let an Agent act. The techniques are different because the attack surface is different. Injected instructions and misused tools do not show up in a standard application test.

Where we can, we test a staging copy that mirrors production. When a production test is needed to prove real impact, it is scoped, gated, and supervised with your team on a live channel. Destructive actions are simulated rather than executed, so we prove the path without causing the damage.

Yes. We test the system you built, including the model, the prompts, the tools, the memory, and the connectors, regardless of which provider or framework sits underneath. Much of the risk lives in how those pieces are wired together, which is exactly what the engagement examines.

A findings report with reproduction steps, business impact, and the attack chain mapped to OWASP classes. Each finding comes with the specific guardrail or design change that closes it, ranked by severity. We then retest to confirm the fixes hold and give you evidence for auditors and your Board.

Adversarial testing is part of demonstrating that a high-risk AI system is safe and resilient. The findings and retest evidence feed directly into the conformity documentation for the EU AI Act and the controls for ISO 42001, so the red team output does double duty as compliance evidence.

Pricing is scoped to the number of Agents, the tools and scopes they hold, and the depth of testing. Most engagements run as fixed-scope exercises with a defined target list and a findings workshop at the end.

A focused exercise against one or two Agents typically runs a few weeks, including scoping, testing, and the findings workshop. Larger Agent estates are phased so high-impact Agents are tested first.

Principal Engineers who build and break Agent systems, working with the same MCP tools, prompts, and scopes your Agents use. The team that tests is the team that reports.

On meaningful change: a new tool, a widened scope, a new model version, or a new data source. Many clients pair an annual deep exercise with lighter regression tests when Agents change.

Each finding maps to a fix: a scope to narrow, a gate to add, a prompt boundary to harden. Where you run AI Governance or Secure Identity 360 with us, fixes feed straight into those programs and re-tests confirm closure.