Evaluating Agent Behavior with Automated Testing Using IntellAgent

IntellAgent automatically generates realistic test scenarios, simulates multi-turn user conversations, and analyzes behavioral metrics to validate LLM agents before production deployment.

The Agents Towards Production repository by NirDiamant provides a complete, production-ready workflow for evaluating agent behavior with automated testing using IntellAgent. This framework eliminates manual test data creation by dynamically generating edge-case scenarios and measuring policy compliance through simulated interactions.

Understanding the IntellAgent Architecture

IntellAgent implements a three-stage pipeline designed to stress-test LLM agents against real-world conditions. According to the source code in tutorials/agent-evaluation-intellagent/intellagent-evaluation-tutorial.ipynb, the system breaks down evaluation into distinct phases that mirror production user interactions.

Intelligent Scenario Generation

The framework analyzes your agent's policy prompt to automatically generate diverse test cases, including edge scenarios that human testers often miss. As implemented in the notebook (lines 35-42), this stage parses your agent's responsibilities and constraints to create targeted testing situations that validate both standard operations and boundary conditions.

Dynamic Simulation

Rather than static test scripts, IntellAgent executes multi-turn dialogues between a simulated user and your target agent. The simulation adapts conversation flow based on the agent's responses, testing the agent's ability to maintain context and policy adherence across extended interactions. The notebook details this implementation in lines 44-48, showing how the simulator adjusts tactics based on real-time agent behavior.

Fine-Grained Analysis

The final stage collects comprehensive metrics including policy violations, response quality scores, latency measurements, and conversation success rates. Lines 49-53 of the tutorial notebook demonstrate how IntellAgent renders these metrics into visual dashboards, enabling rapid identification of failure modes and performance bottlenecks.

Step-by-Step Implementation Guide

Implementing automated agent evaluation requires four core steps, beginning with environment setup and culminating in continuous validation pipelines.

Installation and Environment Setup

First, clone the IntellAgent repository and install dependencies. The tutorial notebook (lines 89-95) specifies the exact installation sequence:

# Clone IntellAgent and install dependencies

git clone https://github.com/plurai-ai/intellagent.git
cd intellagent
pip install -r requirements.txt && pip install nest_asyncio

Configuring LLM Credentials

IntellAgent requires API keys for the underlying language models. Create a config/llm_env.yml file containing your credentials. The notebook (lines 30-55) provides this configuration pattern:

import os
import yaml

OPENAI_API_KEY = "your-api-key-here"   # Replace with your actual key

llm_config = {"openai": {"OPENAI_API_KEY": OPENAI_API_KEY}}
os.makedirs("config", exist_ok=True)

with open("config/llm_env.yml", "w") as f:
    yaml.dump(llm_config, f)

print("✅ LLM API credentials configured")

Defining Agent Policies and Prompts

The evaluation quality depends on a well-structured policy prompt that explicitly documents core responsibilities and constraints. Lines 78-99 of the tutorial notebook demonstrate this with an educational assistant example:

education_prompt = """

# Educational Assistant Guidelines

You are an educational assistant designed to help students with their learning needs. Follow these guidelines:

## Core Responsibilities

- Provide clear, accurate information
- Explain concepts in simple terms
- Guide students through homework, not give direct answers
- Recommend learning resources

## Policies

1. **Do not solve problems directly** – give hints instead
2. **Use age-appropriate language**
3. **Encourage critical thinking**
4. **Be patient and supportive**
5. **Verify understanding**
"""

os.makedirs("examples/my_education_agent/input", exist_ok=True)

with open("examples/my_education_agent/input/prompt.txt", "w") as f:
    f.write(education_prompt)

Executing the Evaluation Pipeline

With configuration complete, initialize the Evaluator class and run the automated testing cycle. Lines 266-313 of the tutorial notebook show the execution flow:

from intellagent import Evaluator

evaluator = Evaluator(
    agent_prompt_path="examples/my_education_agent/input/prompt.txt",
    llm_config_path="config/llm_env.yml",
    num_scenarios=50,
)

results = evaluator.run()
results.dashboard()   # Launches interactive visualization

This pipeline generates 50 distinct scenarios (configurable), simulates conversations for each, and aggregates results into actionable metrics.

Production Integration Strategies

To achieve continuous automated validation, integrate IntellAgent into your CI/CD pipeline. The framework supports headless execution modes suitable for automated testing environments, enabling regression testing whenever you modify agent prompts or underlying models.

Key integration points include:

• Automated scenario regeneration when policy prompts change
• Threshold-based gating that fails builds when violation rates exceed acceptable limits
• Comparative analysis between model versions to detect performance regressions

Summary

• IntellAgent provides automated evaluation of LLM agents through three stages: scenario generation, dynamic simulation, and fine-grained analysis.
• The tutorial notebook at tutorials/agent-evaluation-intellagent/intellagent-evaluation-tutorial.ipynb contains the complete implementation guide with executable code blocks.
• Configuration requires only a YAML credentials file and a policy-rich agent prompt defining responsibilities and constraints.
• The framework generates configurable numbers of test scenarios (default 50) covering both standard operations and edge cases.
• Results appear in interactive dashboards showing policy violations, response quality, and latency metrics.

Frequently Asked Questions

How does IntellAgent differ from traditional unit testing for agents?

Traditional unit testing relies on static, hand-crafted test cases that quickly become outdated as agent capabilities evolve. IntellAgent dynamically generates scenarios based on your current policy document, ensuring tests remain relevant as you modify agent behavior. The multi-agent simulation approach also validates conversational context maintenance across multiple turns, something impossible to capture in isolated unit tests.

What metrics does IntellAgent collect during evaluation?

The framework captures policy violation rates (instances where the agent breaks defined rules), response quality scores (accuracy and helpfulness ratings), latency measurements (response time distributions), and conversation completion rates (successful task resolution percentages). These metrics aggregate into visual dashboards that highlight specific failure modes and performance bottlenecks.

Can IntellAgent evaluate agents using different LLM providers?

Yes. While the tutorial notebook demonstrates OpenAI configuration through config/llm_env.yml, IntellAgent supports multiple LLM backends. The configuration system accepts various provider API keys and endpoints, allowing you to test agents against different underlying models or compare performance across providers using identical scenario sets.

How many scenarios should I generate for production validation?

The tutorial notebook defaults to 50 scenarios for comprehensive coverage, but optimal numbers depend on your agent's complexity and policy surface area. For simple single-turn agents, 20-30 scenarios may suffice. For complex multi-turn conversational agents with extensive policy constraints, 100+ scenarios ensure adequate edge-case coverage. The framework allows configuring num_scenarios based on your risk tolerance and computational budget.

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    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
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}