01
Expert adversaries
We assembled security-literate experts and domain specialists to probe the agent with realistic attack chains, not synthetic edge cases.
Human + AI for safer models
Red-teaming an agentic stack before launch
Ahead of a public agent launch, RDL stood up an expert red-team that surfaced and documented failure modes across tool-use, planning and safety boundaries.
The outcome
2,400+
adversarial cases documented
The challenge
The company was weeks from launching an agent that could browse, write code and take real actions. Internal testing covered the happy path, but they had no structured coverage of adversarial prompts, unsafe tool calls, or multi-step planning failures — the exact places an agent breaks in the real world.
Our approach
02
Structured taxonomy
Every finding was annotated against a shared severity-and-category taxonomy so engineering could triage by risk, not anecdote.
03
Reproducible traces
Each case shipped with a full chain-of-thought trace and reproduction steps, turning red-team findings directly into eval sets.
Results
2,400+
adversarial cases documented
17
critical failure modes caught pre-launch
100%
findings reproducible from traces
“RDL gave us the adversarial coverage our internal team couldn’t — and handed it back as eval sets we still run on every release.”Head of Safety / Autonomous-systems company
Built around your model
Your model deserves better data.
Tell us what you’re training and we’ll show you what high-fidelity human data can do for it.