# config/workflow.yaml
workflows:
  - name: investment_analysis_workflow
    description: Analyze investment opportunities and generate recommendations.
    trigger: user_query
    steps:
      - agent: QueryUnderstandingAgent
        input: user_query
        output: relevant_agents
        next_steps:
          - condition: "len(relevant_agents) > 0"  # Proceed only if relevant agents are found
            next_step: parallel_data_retrieval
          - condition: "len(relevant_agents) == 0"
            next_step: handle_no_relevant_agents

      - name: parallel_data_retrieval  # Parallel execution block
        type: parallel
        steps:
          - agent: "{agent}"  # Dynamically uses the agents identified by QueryUnderstandingAgent
            input: user_query
            output: "{agent}_data"  # Unique output variable for each agent
            loop_variable: agent  #  Iterate over the list of 'relevant_agents'
            loop_source: relevant_agents #  The source of the loop

      - agent: ResultAggregationAgent
        input: "{agent}_data" # this will take a *list* of all outputs.
        # Note: Input is a list comprehension, gathers all outputs.
        #       The template expands to e.g., [DataRetrievalAgent_data, MarketSentimentAgent_data, ...]
        input_mapping: >-  # Explicitly handles combining the multiple input variables.
          [{% for agent in relevant_agents %}
            {{ agent }}_data
          {%- if not loop.last -%},
          {%- endif -%}
          {%- endfor -%}]
        output: aggregated_results
        next_step: newsletter_generation

      - agent: NewsletterLayoutSpecialistAgent
        input: aggregated_results
        output: newsletter_content
        next_step: end

      - name: handle_no_relevant_agents #Handles case with no agents.
        agent: QueryUnderstandingAgent #Could be a different, dedicated agent.
        input: user_query
        output: error_message
        next_step: end # Or could go to a different error handling flow.

      - name: end  # Explicit end step (good practice)
        type: end

  - name: risk_assessment_workflow  # Separate Risk Assessment Workflow
    description: Perform a comprehensive risk assessment.
    trigger: risk_assessment_request
    steps:
      - agent: QueryUnderstandingAgent  # Initial query understanding
        input: risk_assessment_request
        output: relevant_risk_agents
        next_steps:
          - condition: "len(relevant_risk_agents) > 0"
            next_step: parallel_risk_analysis
          - condition: "len(relevant_risk_agents) == 0"
            next_step: handle_no_relevant_agents  # Reuse the error handler

      - name: parallel_risk_analysis
        type: parallel
        steps:
          - agent: "{risk_agent}"  # Iterate over identified risk agents
            input: risk_assessment_request
            output: "{risk_agent}_risk_data"
            loop_variable: risk_agent
            loop_source: relevant_risk_agents

      - agent: RiskAssessmentAgent # Aggregate results from specialized risk agents.
        input: "{risk_agent}_risk_data"
        input_mapping: >-
          [{% for risk_agent in relevant_risk_agents %}
            {{ risk_agent }}_risk_data
          {%- if not loop.last -%},
          {%- endif -%}
          {%- endfor -%}]
        output: overall_risk_assessment
        next_step: end

      - name: end  # Explicit end step
        type: end

  - name: report_generation_workflow
    description: Generate a report based on a user query.
    trigger: user_query
    steps:
      - agent: QueryUnderstandingAgent
        input: user_query
        output: relevant_agents
        next_steps:
          - condition: "len(relevant_agents) > 0"
            next_step: parallel_data_retrieval
          - condition: "len(relevant_agents) == 0"
            next_step: handle_no_relevant_agents

      - name: parallel_data_retrieval
        type: parallel
        steps:
          - agent: "{agent}"
            input: user_query
            output: "{agent}_data"
            loop_variable: agent
            loop_source: relevant_agents

      - agent: ResultAggregationAgent
        input: "{agent}_data"
        input_mapping: >-
          [{% for agent in relevant_agents %}
            {{ agent }}_data
          {%- if not loop.last -%},
          {%- endif -%}
          {%- endfor -%}]
        output: aggregated_results
        next_step: generate_report

      - name: generate_report
        agent: ReportGeneratorAgent
        input: aggregated_results
        output: final_report
        next_step: end

      - name: handle_no_relevant_agents
        agent: QueryUnderstandingAgent
        input: user_query
        output: error_message
        next_step: end

      - name: end
        type: end