parallel-research.yaml

# Parallel Research Workflow
#
# This example demonstrates parallel execution with multiple independent research agents.
# It shows:
# - Parallel execution of independent agents
# - continue_on_error failure mode for resilient data gathering
# - Output aggregation from multiple parallel agents
# - Handling partial failures gracefully
# - Accessing parallel outputs in downstream agents
#
# Usage:
#   conductor run examples/parallel-research.yaml --input topic="AI in healthcare"

workflow:
  name: parallel-research
  description: Research a topic using multiple parallel sources
  version: "1.0.0"
  entry_point: planner

  runtime:
    provider: copilot
    default_model: gpt-5.2
    mcp_servers:
      web-search:
        command: npx
        args: ["-y", "open-websearch@latest"]
        env:
          MODE: stdio
          DEFAULT_SEARCH_ENGINE: duckduckgo
          ALLOWED_SEARCH_ENGINES: duckduckgo,brave,exa
        tools: ["search"]

  input:
    topic:
      type: string
      required: true
      description: The research topic to investigate
    depth:
      type: string
      required: false
      default: "moderate"
      description: Research depth (quick, moderate, comprehensive)

  context:
    mode: explicit  # Only declared inputs available to agents

  limits:
    max_iterations: 20
    timeout_seconds: 600

# Parallel execution groups
parallel:
  - name: parallel_researchers
    description: Research from multiple independent sources in parallel
    agents:
      - academic_researcher
      - web_researcher
      - technical_researcher
    failure_mode: continue_on_error  # Continue even if some sources fail
    routes:
      - to: synthesizer

agents:
  # 1. Planning phase - create research strategy
  - name: planner
    description: Creates a structured research plan
    model: gpt-5.2
    input:
      - workflow.input.topic
      - workflow.input.depth
    tools: []
    prompt: |
      You are a research planning expert. Create a comprehensive research plan for:
      
      Topic: {{ workflow.input.topic }}
      Depth: {{ workflow.input.depth }}
      
      Create a structured plan with:
      1. 3-5 key questions to answer
      2. 3-4 areas to investigate
      3. Types of sources to consult (academic, news, expert opinions, technical docs)
      
      Be specific and actionable.
    output:
      questions:
        type: array
        description: 3-5 key research questions to answer
      areas:
        type: array
        description: 3-4 areas to investigate
      sources:
        type: array
        description: Types of sources to consult
      summary:
        type: string
        description: One-sentence plan summary
    routes:
      - to: parallel_researchers

  # Parallel Agent 1: Academic research
  - name: academic_researcher
    description: Searches academic and scholarly sources
    model: gpt-5.2
    input:
      - workflow.input.topic
      - planner.output
    tools: []  # No tools for testing
    prompt: |
      You are an academic researcher. Research academic and scholarly information on:
      
      Topic: {{ workflow.input.topic }}
      
      Research Questions:
      {{ planner.output.questions | json }}

      Research Areas:
      {{ planner.output.areas | json }}
      
      Provide credible, well-sourced findings based on your knowledge.
    output:
      findings:
        type: array
        description: List of academic findings
      sources:
        type: array
        description: Academic sources consulted
      confidence:
        type: string
        description: Confidence level (high, medium, low)

  # Parallel Agent 2: Web research
  - name: web_researcher
    description: Searches web sources and news
    model: gpt-5.2
    input:
      - workflow.input.topic
      - planner.output
    tools: []  # No tools for testing
    prompt: |
      You are a web researcher. Research current and practical information on:
      
      Topic: {{ workflow.input.topic }}
      
      Research Questions:
      {{ planner.output.questions | json }}

      Research Areas:
      {{ planner.output.areas | json }}
      
      Provide findings based on your knowledge of recent and authoritative sources.
    output:
      findings:
        type: array
        description: List of web findings
      sources:
        type: array
        description: Web sources consulted
      confidence:
        type: string
        description: Confidence level (high, medium, low)

  # Parallel Agent 3: Technical research
  - name: technical_researcher
    description: Searches technical documentation and specifications
    model: gpt-5.2
    input:
      - workflow.input.topic
      - planner.output
    tools: []  # No tools for testing
    prompt: |
      You are a technical researcher. Research technical information on:
      
      Topic: {{ workflow.input.topic }}
      
      Research Questions:
      {{ planner.output.questions | json }}

      Research Areas:
      {{ planner.output.areas | json }}
      
      Provide findings based on your technical knowledge.
    output:
      findings:
        type: array
        description: List of technical findings
      sources:
        type: array
        description: Technical sources consulted
      confidence:
        type: string
        description: Confidence level (high, medium, low)

  # 3. Synthesis phase - combine parallel results
  - name: synthesizer
    description: Synthesizes findings from all parallel researchers
    model: gpt-5.2
    input:
      - workflow.input.topic
      - planner.output
      - parallel_researchers.outputs
      - parallel_researchers.errors
    tools: []
    prompt: |
      You are a research synthesizer. Combine findings from multiple parallel research sources.
      
      Topic: {{ workflow.input.topic }}
      
      Research Plan Summary: {{ planner.output.summary }}
      
      {% if parallel_researchers.errors %}
      ⚠️  Warning: Some research sources failed:
      {% for agent, error in parallel_researchers.errors.items() %}
      - {{ agent }}: {{ error.message }}
      {% endfor %}
      
      {% endif %}
      Research Results:
      
      {% if 'academic_researcher' in parallel_researchers.outputs %}
      === Academic Research ===
      {{ parallel_researchers.outputs.academic_researcher.findings | json }}
      Sources: {{ parallel_researchers.outputs.academic_researcher.sources | length }}
      Confidence: {{ parallel_researchers.outputs.academic_researcher.confidence }}
      
      {% endif %}
      {% if 'web_researcher' in parallel_researchers.outputs %}
      === Web Research ===
      {{ parallel_researchers.outputs.web_researcher.findings | json }}
      Sources: {{ parallel_researchers.outputs.web_researcher.sources | length }}
      Confidence: {{ parallel_researchers.outputs.web_researcher.confidence }}
      
      {% endif %}
      {% if 'technical_researcher' in parallel_researchers.outputs %}
      === Technical Research ===
      {{ parallel_researchers.outputs.technical_researcher.findings | json }}
      Sources: {{ parallel_researchers.outputs.technical_researcher.sources | length }}
      Confidence: {{ parallel_researchers.outputs.technical_researcher.confidence }}
      
      {% endif %}
      Create a comprehensive synthesis that:
      1. Combines findings from all successful sources
      2. Notes any discrepancies or conflicting information
      3. Highlights key insights and patterns
      4. Addresses the original research questions
      5. Acknowledges failed sources and potential gaps
      
      Provide an executive summary and detailed analysis.
    output:
      executive_summary:
        type: string
        description: Brief executive summary (2-3 sentences)
      key_insights:
        type: array
        description: Top 5 key insights from all sources
      synthesis:
        type: string
        description: Detailed synthesis of all findings
      sources_analyzed:
        type: number
        description: Total number of sources analyzed
      research_quality:
        type: string
        description: Overall quality assessment
    routes:
      - to: quality_checker

  # 4. Quality check phase - evaluate research completeness
  - name: quality_checker
    description: Evaluates research quality and completeness
    model: gpt-5.2
    input:
      - workflow.input.topic
      - planner.output
      - synthesizer.output
      - parallel_researchers.outputs
    tools: []
    prompt: |
      You are a research quality evaluator. Assess the quality and completeness of this research:
      
      Topic: {{ workflow.input.topic }}
      
      Original Research Questions:
      {{ planner.output.questions | json }}
      
      Executive Summary:
      {{ synthesizer.output.executive_summary }}
      
      Key Insights:
      {{ synthesizer.output.key_insights | json }}
      
      Number of Sources: {{ synthesizer.output.sources_analyzed }}
      Research Quality: {{ synthesizer.output.research_quality }}
      
      Number of Successful Researchers: {{ parallel_researchers.outputs | length }} / 3
      
      Evaluate:
      1. Were the research questions adequately answered?
      2. Is the coverage comprehensive?
      3. Are findings well-supported by sources?
      4. Are there any significant gaps?
      5. Is the synthesis clear and actionable?
      
      Provide a quality score (1-10) and specific feedback.
    output:
      quality_score:
        type: number
        description: Overall quality score (1-10)
      questions_answered:
        type: number
        description: Number of research questions answered (0-5)
      coverage_complete:
        type: boolean
        description: Whether coverage is comprehensive
      gaps:
        type: array
        description: Identified gaps or missing information
      recommendation:
        type: string
        description: Accept, revise, or expand research
    routes:
      - to: $end
        when: "{{ output.quality_score >= 7 }}"
      - to: planner
        when: "{{ context.iteration < 3 }}"
        output:
          feedback: "{{ quality_checker.output.gaps | json }}"
      - to: $end

output:
  topic: "{{ workflow.input.topic }}"
  executive_summary: "{{ synthesizer.output.executive_summary }}"
  key_insights: "{{ synthesizer.output.key_insights | json }}"
  synthesis: "{{ synthesizer.output.synthesis }}"
  quality_score: "{{ quality_checker.output.quality_score }}"
  sources_analyzed: "{{ synthesizer.output.sources_analyzed }}"
  research_quality: "{{ synthesizer.output.research_quality }}"
  researchers_succeeded: "{{ parallel_researchers.outputs | length }}"
  researchers_failed: "{{ parallel_researchers.errors | length }}"
  recommendation: "{{ quality_checker.output.recommendation }}"