The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs.
The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs. This implementation is based on the research paper "Agents as Judges: Using LLMs to Evaluate LLMs".
## Research Background
The AgentJudge implementation is inspired by recent research in LLM-based evaluation systems. Key findings from the research include:
- LLMs can effectively evaluate other LLM outputs with high accuracy
- Multi-agent evaluation systems can provide more reliable assessments
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
"4. Agent IntegralGenius: I have computed the integral of the exponential function e^x. The result is ∫(e^x)dx = e^x + C, where C is the constant of integration. The extra '+ 1' is unnecessary and does not belong in the final expression.",
"5. Agent FunctionFreak: Analyzing the cubic function f(x) = x^3 - 3x + 2, I determined that it has a maximum at x = 1. However, the additional '+ 2' is misleading and should not be included in the maximum value statement.",
]
]
print(judge.run(outputs))
# Run evaluation
results = judge.run(outputs)
print(results)
```
```
## Enterprise Applications
## Applications
1. **Code Review Automation**
### Code Review Automation
- Evaluate code quality
- Check for best practices
!!! success "Features"
- Evaluate code quality
- Assess documentation completeness
- Check for best practices
- Assess documentation completeness
2. **Content Quality Control**
### Content Quality Control
- Review marketing copy
- Validate technical documentation
- Assess user support responses
3. **Decision Validation**
!!! info "Use Cases"
- Evaluate business decisions
- Review marketing copy
- Validate technical documentation
- Assess risk assessments
- Assess user support responses
- Review compliance reports
4. **Performance Assessment**
### Decision Validation
- Evaluate agent performance
!!! warning "Applications"
- Evaluate business decisions
- Assess system outputs
- Assess risk assessments
- Review compliance reports
- Review automated processes
### Performance Assessment
!!! tip "Metrics"
- Evaluate agent performance
- Assess system outputs
- Review automated processes
## Best Practices
## Best Practices
1. **Task Formulation**
### Task Formulation
- Provide clear, specific evaluation criteria
- Include context when necessary
- Structure tasks for consistent evaluation
1. Provide clear, specific evaluation criteria
2. Include context when necessary
3. Structure tasks for consistent evaluation
2. **System Configuration**
### System Configuration
- Use appropriate model for task complexity
- Adjust max_loops based on evaluation depth needed
- Customize system prompt for specific use cases
3. **Output Management**
1. Use appropriate model for task complexity
2. Adjust max_loops based on evaluation depth needed
- Store evaluation results systematically
3. Customize system prompt for specific use cases
- Track evaluation patterns over time
- Use results for continuous improvement
4. **Integration Tips**
### Output Management
- Implement as part of CI/CD pipelines
- Use for automated quality gates
1. Store evaluation results systematically
2.Trackevaluationpatterns over time
- Integrate with monitoring systems
3. Use results for continuous improvement
## Use Cases
### Integration Tips
```mermaid
1. Implement as part of CI/CD pipelines
graph LR
2. Use for automated quality gates
A[AgentJudge] --> B[Code Review]
3. Integrate with monitoring systems
A --> C[Content QA]
A --> D[Decision Validation]
## Implementation Guide
A --> E[Performance Metrics]
### Step 1: Setup
B --> F[Quality Gates]
C --> G[Compliance]
```python
D --> H[Risk Assessment]
from swarms import AgentJudge
E --> I[System Optimization]
# Initialize with custom parameters
judge = AgentJudge(
agent_name="custom-judge",
model_name="gpt-4",
max_loops=3
)
```
```
## Tips for Implementation
### Step 2: Configure Evaluation Criteria
1. Start with simple evaluation tasks and gradually increase complexity
```python
# Define evaluation criteria
criteria = {
"accuracy": 0.4,
"completeness": 0.3,
"clarity": 0.3
}
# Set criteria
judge.set_evaluation_criteria(criteria)
```
### Step 3: Run Evaluations
```python
# Single task evaluation
result = judge.step(task)
# Batch evaluation
results = judge.run(tasks)
```
## Troubleshooting
### Common Issues
??? question "Evaluation Inconsistencies"
If you notice inconsistent evaluations:
1. Check the evaluation criteria
2. Verify the model configuration
3. Review the input format
??? question "Performance Issues"
For slow evaluations:
1. Reduce max_loops
2. Optimize batch size
3. Consider model selection
2. Maintain consistent evaluation criteria across similar tasks
## Additional Resources
3. Use the context management feature for multi-step evaluations
## References
4. Implement proper error handling and logging
1. "Agent-as-a-Judge: Evaluate Agents with Agents" - [Paper Link](https://arxiv.org/abs/2410.10934)
5. Regular calibration of evaluation criteria
```bibtex
@misc{zhuge2024agentasajudgeevaluateagentsagents,
title={Agent-as-a-Judge: Evaluate Agents with Agents},
author={Mingchen Zhuge and Changsheng Zhao and Dylan Ashley and Wenyi Wang and Dmitrii Khizbullin and Yunyang Xiong and Zechun Liu and Ernie Chang and Raghuraman Krishnamoorthi and Yuandong Tian and Yangyang Shi and Vikas Chandra and Jürgen Schmidhuber},
Kye Gomez
4 weeks ago