Agent Judge¶

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 serves as an impartial evaluator that can: - Assess the quality and correctness of agent outputs

Provide structured feedback and scoring
Maintain context across multiple evaluations
Generate detailed analysis reports

Architecture¶

graph TD
    A[Input Tasks] --> B[AgentJudge]
    B --> C[Agent Core]
    C --> D[LLM Model]
    D --> E[Response Generation]
    E --> F[Context Management]
    F --> G[Output]

    subgraph "Evaluation Flow"
    H[Task Analysis] --> I[Quality Assessment]
    I --> J[Feedback Generation]
    J --> K[Score Assignment]
    end

    B --> H
    K --> G

Parameters¶

Parameter	Type	Default	Description
`agent_name`	str	"agent-judge-01"	Unique identifier for the judge agent
`system_prompt`	str	AGENT_JUDGE_PROMPT	System instructions for the agent
`model_name`	str	"openai/o1"	LLM model to use for evaluation
`max_loops`	int	1	Maximum number of evaluation iterations

Methods¶

Method	Description	Parameters	Returns
`step()`	Processes a single batch of tasks	`tasks: List[str]`	`str`
`run()`	Executes multiple evaluation iterations	`tasks: List[str]`	`List[str]`

Code Example¶

from swarms import AgentJudge


judge = AgentJudge(model_name="gpt-4o", max_loops=1)


outputs = [
    "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).",
    "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))

Enterprise Applications¶

Code Review Automation
Evaluate code quality
Check for best practices
Assess documentation completeness
Content Quality Control
Review marketing copy
Validate technical documentation
Assess user support responses
Decision Validation
Evaluate business decisions
Assess risk assessments
Review compliance reports
Performance Assessment
Evaluate agent performance
Assess system outputs
Review automated processes

Best Practices¶

Task Formulation
Provide clear, specific evaluation criteria
Include context when necessary
Structure tasks for consistent evaluation
System Configuration
Use appropriate model for task complexity
Adjust max_loops based on evaluation depth needed
Customize system prompt for specific use cases
Output Management
Store evaluation results systematically
Track evaluation patterns over time
Use results for continuous improvement
Integration Tips
Implement as part of CI/CD pipelines
Use for automated quality gates
Integrate with monitoring systems

Use Cases¶

graph LR
    A[AgentJudge] --> B[Code Review]
    A --> C[Content QA]
    A --> D[Decision Validation]
    A --> E[Performance Metrics]

    B --> F[Quality Gates]
    C --> G[Compliance]
    D --> H[Risk Assessment]
    E --> I[System Optimization]

Tips for Implementation¶

Start with simple evaluation tasks and gradually increase complexity
Maintain consistent evaluation criteria across similar tasks
Use the context management feature for multi-step evaluations
Implement proper error handling and logging
Regular calibration of evaluation criteria