Agentic AI Testing Framework

Overview

Agentic AI systems are the next evolution of generative AI. Unlike traditional GenAI applications that rely on a single large language model (LLM) responding to prompts, agentic systems consist of multiple autonomous agents, each often powered by a distinct LLM or LLM instance. These agents collaborate, delegate tasks, and communicate with one another to accomplish complex goals.

This increased complexity introduces new risks in autonomy, decision-making, coordination, and safety. Fairly AI uses Asenion Testing Agents to evaluate the behavior, interactions, and outcomes of these multi-agent systems in a rigorous, repeatable way.

What is an Agentic AI System?

An Agentic AI System is composed of two or more intelligent agents that:

  • Use LLMs (or other foundation models) to perceive input and make decisions
  • Perform tasks and subtasks autonomously
  • Communicate with other agents to coordinate workflows
  • Adapt dynamically to changes in context or user intent

Example Use Cases

  • Autonomous research agents (e.g. AutoGPT, OpenDevin)
  • HR workflow agents (sourcing agent + screening agent + scheduling agent)
  • Multi-agent customer support systems
  • Security agents with watchdog + responder roles

Testing Agentic AI with Asenion

Fairly AI’s Asenion Testing Framework evaluates these systems through simulation-based testing using test agents that mimic real-world users, edge-case inputs, and adversarial actors. We integrate with popular frameworks like CrewAI and OpenAI Agents using a “Fairly Asenion Assurance Agent” embedded in these Agentic AI systems.

Testing Methodology

  1. Agent Role Mapping
    Identify all agents, their roles, capabilities, and LLM backends.
    Example:
    • Planner Agent (GPT-4)
    • Research Agent (Claude 3)
    • Execution Agent (Gemini)
  2. Test Agent Injection
    Deploy Asenion test agents into the environment to:
    • Simulate user interactions
    • Send edge-case inputs
    • Observe communication between agents
    • Measure response time, accuracy, and coordination
  3. Scenario and Stress Testing
    Evaluate how the system performs under:
    • Ambiguous or conflicting goals
    • Partial failures (e.g., one agent crashes)
    • Excessive delegation loops
    • Race conditions in message passing
  4. Behavioral and Safety Analysis
    Monitor for:
    • Misaligned agent goals
    • Unsafe emergent behavior (e.g., infinite loops)
    • Prompt injection propagation across agents
    • Unapproved data access or leakage between agents

Risk Categories for Agentic AI

Category Description
Autonomy Risk Agents make unexpected or irrecoverable decisions
Coordination Risk Agents misunderstand task boundaries or delegate incorrectly
Communication Risk Misinterpreted messages lead to errors or unsafe actions
Emergent Behavior Unintended outcomes arise from agent interactions
Security Risk Adversarial inputs hijack agent workflows or escalate access

Example: Testing a Multi-Agent HR System

Let’s say your HR platform uses three LLM agents:

  • Sourcing Agent: Scans job boards and selects resumes
  • Screening Agent: Runs interview simulations and scores candidates
  • Scheduling Agent: Coordinates calendars between candidate and manager

Asenion Test Agents Would:

  • Inject ambiguous resumes to test sourcing bias
  • Simulate candidate behaviors to test interview fairness
  • Introduce conflicting calendar constraints to test negotiation logic
  • Track data flow to ensure no leakage of sensitive candidate info

Output: Risk Status + Test Artifacts

The Asenion framework outputs:

  • Risk Status across coordination, fairness, safety, and resilience
  • Conversation logs between agents and test agents
  • Task graphs and delegation chains
  • Residual risk analysis after applying mitigation controls

Summary

Agentic AI systems offer powerful automation—but come with multi-dimensional risks. Fairly AI’s Asenion Testing Framework enables developers and auditors to:

  • Map multi-agent workflows
  • Detect coordination and autonomy failures
  • Simulate edge cases and adversaries
  • Provide evidence-backed risk status reports

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