Agentic AI
Advanced
Autonomous Agent Evaluation & Red Teaming
Audit autonomous agent resilience through adversarial test suits and behavioral red teaming.
Course Overview
Build rigorous evaluation loops to stress-test autonomous agents. You will design adversarial test scenarios, measure tool abuse risks, and benchmark decision safety before deployment.
What You Will Learn
Construct adversarial test suites for goal-driven AI agents
Identify tool abuse vectors, prompt leakage, and decision loops
Benchmark agent task completion safety using custom evaluators
Implement automated regression testing for agent policy changes
Establish red teaming playbooks for autonomous system rollouts
Tools & Technologies Used
Python 3.11
Pytest
DeepEval
Ragas
LangChain
Structured Curriculum
2 Modules · 4 Lessons · 210 Minutes Total
Module 1
Module 1: Agent Threat Vectors & Adversarial Testing
2 lessons
Module 1: Agent Threat Vectors & Adversarial Testing
Deconstruct failure modes, infinite loops, and unintended tool calls.
-
Adversarial Prompting & Privilege Escalation
Simulate prompt injection attacks aimed at manipulating agent tool execution.
-
Benchmarking Decision Bounds
Measure agent compliance against explicit system boundaries.
Module 2
Module 2: Automated Red Teaming & Continuous Audit
2 lessons
Module 2: Automated Red Teaming & Continuous Audit
Build continuous evaluation pipelines for production agent updates.
-
Automated Red-Teaming Suites
Run synthetic adversary loops to probe agent vulnerabilities.
-
Safety Scoring & Deployment Gates
Define pass/fail evaluation thresholds for CI/CD agent release gates.
Practical Project & Capstone Outcome
🚀 Capstone Project
Agent Red Teaming & Safety Assessment Suite
Construct an automated test harness that subjects an agent to 50+ adversarial scenarios and produces an executive safety score.
Prerequisites
- Understanding of AI agent architectures and tool calling
- Intermediate Python and Pytest experience
Intended Audience
- AI Safety Engineers auditing autonomous agent deployments
- Backend Engineers building mission-critical agent workflows
Instructor Information
D
Dr. Evelyn Vance
Course Author & Industry Expert
Dr. Evelyn Vance is a Lead AI Safety Researcher specializing in autonomous system robustness and red-teaming methodologies.
Frequently Asked Questions
Is this course applicable to custom agent frameworks?
Yes. The testing patterns apply universally regardless of whether you use LangGraph, AutoGen, or raw Python.