Evals

Evaluation (Evals) in AgentScope AI is a critical component that ensures the performance, accuracy, and reliability of deployed agents and workflows. By implementing structured evaluation methodologies, AgentScope AI enables developers to assess their models and tools systematically, making data-driven improvements.

This document outlines how to set up, configure, and run Evals within AgentScope AI for continuous performance monitoring and enhancement.

Setting Up Evals

Before running evaluations, ensure that your AgentScope AI deployment is properly configured with the necessary datasets and benchmarking tools.

Prerequisites

• A deployed instance of AgentScope AI

• Access to relevant datasets and evaluation metrics

• Logging and monitoring enabled for detailed analysis

Configuration

To configure Evals, define the evaluation criteria in your config.json or a dedicated YAML file:

{
  "evals": {
    "metrics": ["accuracy", "latency", "throughput"],
    "datasets": ["benchmark_set_1", "custom_dataset_2"],
    "iterations": 100,
    "logging": true
  }
}

Running Evals

Evals can be triggered manually or automatically as part of your CI/CD pipeline.

Manual Execution

To run an evaluation manually, use the following command:

agent_scope eval --config eval_config.json

Automated Execution

Integrate Evals into your CI/CD pipeline to ensure continuous validation of performance and improvements.

Example GitHub Actions workflow:

name: AgentScope AI Evals
on: [push, pull_request]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Repository
        uses: actions/checkout@v3
      
      - name: Install Dependencies
        run: npm install agent_scope
      
      - name: Run Evaluations
        run: agent_scope eval --config eval_config.json

Analyzing Evaluation Results

Once evaluations are completed, results are logged and can be analyzed using built-in visualization tools or exported for further analysis.

Sample Output

{
  "accuracy": 95.3,
  "latency": 120,
  "throughput": 500,
  "errors": 2
}

Debugging Poor Performance

If an agent or workflow performs below expectations, consider:

• Adjusting hyperparameters

• Refining dataset selection

• Debugging tool integration issues