Agents Index

Agents are reusable AI components with defined capabilities, tools, and instructions.

Available Agents

Evaluator Agent

Path: agents/evaluator-agent/evaluator-agent.md Purpose: Assess the quality of LLM-generated responses

Capabilities:

• Direct scoring against rubrics
• Pairwise comparison of responses
• Criteria extraction from task descriptions
• Rubric generation for evaluation

Tools Used:

• directScore
• pairwiseCompare
• extractCriteria
• generateRubric

Best For:

• Quality gates in content pipelines
• Model comparison studies
• RLHF preference data generation
• Output validation before delivery

Research Agent

Path: agents/research-agent/research-agent.md Purpose: Gather, verify, and synthesize information from multiple sources

Capabilities:

• Web search and result analysis
• URL content extraction
• Claim extraction and verification
• Research synthesis

Tools Used:

• webSearch
• readUrl
• extractClaims
• verifyClaim
• synthesize

Best For:

• Knowledge base building
• Fact checking
• Market research
• Technical documentation

Orchestrator Agent

Path: agents/orchestrator-agent/orchestrator-agent.md Purpose: Coordinate multi-agent workflows for complex tasks

Capabilities:

• Task decomposition and assignment
• Parallel task execution
• Result synthesis
• Error handling and recovery

Tools Used:

• delegateToAgent
• parallelExecution
• waitForCompletion
• synthesizeResults
• handleError

Best For:

• Complex multi-step tasks
• Cross-capability workflows
• Quality-assured pipelines
• Long-running operations

Agent Interaction Patterns

Sequential Pipeline

Input → Agent A → Agent B → Agent C → Output

Use when each step depends on the previous.

Parallel Fan-Out

        ┌→ Agent A ─┐
Input ──┼→ Agent B ──┼→ Synthesis → Output
        └→ Agent C ─┘

Use for independent subtasks that can run concurrently.

Iterative Refinement

Input → Agent → Evaluator ─┬→ Output (if pass)
                           └→ Agent (if fail, with feedback)

Use for quality-critical outputs.

Adding New Agents

1. Create agent directory: agents/<agent-name>/
2. Create main file: agents/<agent-name>/<agent-name>.md
3. Define:

• Purpose and role
• System instructions
• Tool assignments
• Configuration options
• Usage examples

1. Update this index
2. Register with orchestrator if applicable