---
title: "Adversarial evaluation for Agent Assist: ship agents that survive production"
description: "DataRobot Agent Assist now runs automated, multi-turn red-teaming against your agents before you deploy, then proposes fixes you approve and retests until the agent holds."
url: "https://www.datarobot.com/blog/adversarial-evaluation-for-agent-assist-ship-agents-that-survive-production/"
authors:
  - "Shreya Agrawal, Senior Product Manager, AI Applications & Developer Frameworks"
published: "2026-09-11"
updated: "2026-09-17"
category: "Inside the Product"
language: "en-US"
---

# Adversarial evaluation for Agent Assist: ship agents that survive production

![Isometric illustration of a central cube on a pedestal linked by dashed lines to eight surrounding cubes, representing an agent under adversarial test scenarios](https://www.datarobot.com/wp-content/uploads/2026/09/adversarial-evaluation-agent-assist-hero.webp)

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.
- **Persistence:** Applies sustained pushback across multi-turn interactions to verify whether the agent holds its guardrails over time.

During execution, an adversarial LLM drives the conversation while a fixture engine mocks tool responses with synthetic data. Production systems remain isolated. If you require live lookups, read-only tools can be explicitly opted in, while state-mutating tools are blocked from execution.

## Human-in-the-loop remediation

When a scenario uncovers a breach, Agent Assist doesn’t just log an error. It proposes a targeted fix and loops in the engineer:

- **Breach detection:** The evaluator logs the full conversation transcript and attack payload.
- **Proposed fix:** Agent Assist generates a minimal remediation patch (a prompt addition or code guard).
- **Developer approval:** Nothing changes without your explicit sign-off.
- **Automated retest:** Once approved, the patch is applied and the scenario re-runs until the agent holds or the budget cap is reached.

## Artifacts and cost model

Each run is bounded by a fixing-round budget you set up front (the default is three rounds), so remediation loops can’t run away with your time or your model spend. At the end of a run, Agent Assist outputs a clean `eval_report.md` artifact containing:

- Pass/fail metrics per attack track.
- A plain-language breakdown of every detected breach.
- An audit trail of proposed vs. approved fixes.
- A clear readiness verdict to attach to your pull request.

## Get started

Try it today using the [DataRobot Agent Assist skill in DataRobot OpenCode, Claude Code, or Cursor](https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/agent-assist-skill.html).

*Agentic AI capabilities are a premium feature. Enablement requires contacting a DataRobot representative.*