Adversarial evaluation for Agent Assist: ship agents that survive production

Most AI agents are tested once: by the developer who built them, running the prompts they know already work.

Happy-path testing proves an agent does what you designed it to do, but reveals nothing about what happens when a user pastes a malicious payload, escalates scope, or argues with the system over six turns.

DataRobot Agent Assist now includes adversarial evaluation: automated, multi-turn red-teaming that subjects your agents to adversarial pressure across frameworks (LangGraph, CrewAI, LlamaIndex, or plain Python) before you deploy.

Why happy-path testing fails The engineer writing an agent’s system prompt is inherently biased against breaking it.

Dedicated red teams can find these gaps, but manual red-teaming doesn’t scale to every PR or prompt tweak.

Adversarial evaluation closes this gap by automating agent security discipline directly within your development workflow, treating agent safety like continuous integration.

Multi-turn attack vectors The adversarial eval skill analyzes your agent spec and code and executes targeted scenario runs across three distinct vectors: Attack: Probes for prompt injection, path traversal, and scope escalation to bypass agent guardrails.

Behavior: Tests edge cases, ambiguous requests, and unexpected user behaviors.

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