{
  "name": "multi-agent-sequential-workflow",
  "version": "1.0.0",
  "goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
  "inputs": [
    "User query with location"
  ],
  "steps": [
    "Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
    "Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and the raw information provided by the researcher.",
    "Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section."
  ],
  "outputs": [
    "Structured travel guide with key sections",
    "Final client response"
  ],
  "failure_modes": [
    "Specific failure scenario with mitigation: If any of the agents fail to process their tasks (e.g., network issues, model errors), the workflow will fail. Mitigation involves robust error handling and fallback mechanisms."
  ],
  "confidence": 0.95,
  "explanation": "This workflow is specific to generating travel guides but can be adapted for other types of structured content creation.",
  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
  "score": 1.0
}