Complete Execution Output (JSON):
{
"execution_summary": {
"completed_tasks": 1,
"dependencies_detected": false,
"end_time": "2025-07-07T10:50:44.295858",
"execution_mode": "distributed",
"start_time": "2025-07-07T10:50:38.141227",
"total_tasks": 2
},
"process_results": {
"execution_details": {
"actual_result": {
"output": "Debug: Looking for search results...\nFound: search_tavily = {\"error\": null, \"result\": [{\"text\": \"Detailed Resu...\nParsed result_data: \u003cclass \u0027list\u0027\u003e\n__OUTPUTS__ {\"search_query\": \"machine learning trends\", \"total_results\": 0, \"results\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.259332,
"end_time": "2025-07-07T10:50:44.333077",
"message_sent": true,
"start_time": "2025-07-07T10:50:44.073745",
"timestamp": "2025-07-07T10:50:44.333077",
"worker_executed": true,
"workers_notified": true
},
"output": "Debug: Looking for search results...\nFound: search_tavily = {\"error\": null, \"result\": [{\"text\": \"Detailed Resu...\nParsed result_data: \u003cclass \u0027list\u0027\u003e\n__OUTPUTS__ {\"search_query\": \"machine learning trends\", \"total_results\": 0, \"results\": []}\n",
"results": [],
"return_code": 0,
"search_query": "machine learning trends",
"status": "completed",
"stderr": "",
"task_id": "process_results",
"total_results": 0
},
"search_tavily": {
"error": null,
"execution_details": {
"actual_result": {
"error": null,
"result": [
{
"annotations": null,
"text": "Detailed Results:\n\nTitle: Real-World Examples of Machine Learning (ML) - Tableau\nURL: https://www.tableau.com/learn/articles/machine-learning-examples\nContent: As more organizations and people rely on machine learning models to manage growing volumes of data, instances of machine learning are occurring in front of and around us daily\u2014whether we notice or not. What\u2019s exciting to see is how it\u2019s improving our quality of life, supporting quicker and more effective execution of some business operations and industries, and uncovering patterns that humans are likely to miss. Here are examples of machine learning at work in our daily life that provide value in many ways\u2014some large and some small. Facial recognition Facial recognition is one of the more obvious applications of machine learning. Targeted marketing with retail uses machine learning to group customers based on buying habits or demographic similarities, and by extrapolating what one person may want from someone else\u2019s purchases.\n\nTitle: What is the Future of Machine Learning? - Mission Cloud Services\nURL: https://www.missioncloud.com/blog/what-is-the-future-of-machine-learning\nContent: Machine learning (ML) is frequently confused with generative AI. This architecture allows deep learning models to extract features from raw data, making them effective for complex tasks such as image and speech recognition. Unlike traditional ML methods that often require manual feature extraction, DL can learn directly from unstructured data like images or text, enhancing its scalability and performance in handling large datasets. Enhanced AI Applications: The rise of multimodal machine learning enables systems to process diverse data types simultaneously, improving interaction capabilities. Reliable machine learning models depend on high-quality data. Mission\u0027s AI-powered governance and compliance features facilitate oversight of ML systems, helping mitigate data privacy and bias risks. Businesses can capitalize on machine learning by leveraging Mission\u2019s AI/ML expertise.\n\nTitle: AI and Machine Learning Trends in 2025 - DATAVERSITY\nURL: https://www.dataversity.net/ai-and-machine-learning-trends-in-2025/\nContent: Autonomous vehicles are expected to navigate more complex environments with unprecedented precision, thanks in part to sophisticated AI algorithms that can process vast amounts of data in real time. This influx of data provides unprecedented opportunities for ML to learn and adapt with more precision and real-time insights, paving the way for innovations in predictive analytics, automation, and user personalization. AI and ML empower e-commerce platforms to continue to enhance the personalized shopping experiences by analyzing vast amounts of customer data. As the volume of biological data grows exponentially, AI and ML are becoming indispensable tools for managing and interpreting this complex information. By analyzing vast amounts of educational data, AI can predict students\u2019 learning trajectories, thereby implementing timely interventions to help struggling learners.",
"type": "text"
}
],
"status": "success",
"tool_name": "tavily-search"
},
"duration_seconds": 5.281751,
"end_time": "2025-07-07T10:50:44.358915",
"message_sent": true,
"start_time": "2025-07-07T10:50:39.077164",
"timestamp": "2025-07-07T10:50:44.358915",
"worker_executed": true,
"workers_notified": true
},
"output": "Task completed successfully",
"result": [
{
"annotations": null,
"text": "Detailed Results:\n\nTitle: Real-World Examples of Machine Learning (ML) - Tableau\nURL: https://www.tableau.com/learn/articles/machine-learning-examples\nContent: As more organizations and people rely on machine learning models to manage growing volumes of data, instances of machine learning are occurring in front of and around us daily\u2014whether we notice or not. What\u2019s exciting to see is how it\u2019s improving our quality of life, supporting quicker and more effective execution of some business operations and industries, and uncovering patterns that humans are likely to miss. Here are examples of machine learning at work in our daily life that provide value in many ways\u2014some large and some small. Facial recognition Facial recognition is one of the more obvious applications of machine learning. Targeted marketing with retail uses machine learning to group customers based on buying habits or demographic similarities, and by extrapolating what one person may want from someone else\u2019s purchases.\n\nTitle: What is the Future of Machine Learning? - Mission Cloud Services\nURL: https://www.missioncloud.com/blog/what-is-the-future-of-machine-learning\nContent: Machine learning (ML) is frequently confused with generative AI. This architecture allows deep learning models to extract features from raw data, making them effective for complex tasks such as image and speech recognition. Unlike traditional ML methods that often require manual feature extraction, DL can learn directly from unstructured data like images or text, enhancing its scalability and performance in handling large datasets. Enhanced AI Applications: The rise of multimodal machine learning enables systems to process diverse data types simultaneously, improving interaction capabilities. Reliable machine learning models depend on high-quality data. Mission\u0027s AI-powered governance and compliance features facilitate oversight of ML systems, helping mitigate data privacy and bias risks. Businesses can capitalize on machine learning by leveraging Mission\u2019s AI/ML expertise.\n\nTitle: AI and Machine Learning Trends in 2025 - DATAVERSITY\nURL: https://www.dataversity.net/ai-and-machine-learning-trends-in-2025/\nContent: Autonomous vehicles are expected to navigate more complex environments with unprecedented precision, thanks in part to sophisticated AI algorithms that can process vast amounts of data in real time. This influx of data provides unprecedented opportunities for ML to learn and adapt with more precision and real-time insights, paving the way for innovations in predictive analytics, automation, and user personalization. AI and ML empower e-commerce platforms to continue to enhance the personalized shopping experiences by analyzing vast amounts of customer data. As the volume of biological data grows exponentially, AI and ML are becoming indispensable tools for managing and interpreting this complex information. By analyzing vast amounts of educational data, AI can predict students\u2019 learning trajectories, thereby implementing timely interventions to help struggling learners.",
"type": "text"
}
],
"return_code": 0,
"status": "success",
"stderr": "",
"task_id": "search_tavily",
"tool_name": "tavily-search"
},
"status": "FAILED",
"task_outputs": {
"process_results": {
"execution_details": {
"actual_result": {
"output": "Debug: Looking for search results...\nFound: search_tavily = {\"error\": null, \"result\": [{\"text\": \"Detailed Resu...\nParsed result_data: \u003cclass \u0027list\u0027\u003e\n__OUTPUTS__ {\"search_query\": \"machine learning trends\", \"total_results\": 0, \"results\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.259332,
"end_time": "2025-07-07T10:50:44.333077",
"message_sent": true,
"start_time": "2025-07-07T10:50:44.073745",
"timestamp": "2025-07-07T10:50:44.333077",
"worker_executed": true,
"workers_notified": true
},
"output": "Debug: Looking for search results...\nFound: search_tavily = {\"error\": null, \"result\": [{\"text\": \"Detailed Resu...\nParsed result_data: \u003cclass \u0027list\u0027\u003e\n__OUTPUTS__ {\"search_query\": \"machine learning trends\", \"total_results\": 0, \"results\": []}\n",
"results": [],
"return_code": 0,
"search_query": "machine learning trends",
"status": "completed",
"stderr": "",
"task_id": "process_results",
"total_results": 0
},
"search_tavily": {
"error": null,
"execution_details": {
"actual_result": {
"error": null,
"result": [
{
"annotations": null,
"text": "Detailed Results:\n\nTitle: Real-World Examples of Machine Learning (ML) - Tableau\nURL: https://www.tableau.com/learn/articles/machine-learning-examples\nContent: As more organizations and people rely on machine learning models to manage growing volumes of data, instances of machine learning are occurring in front of and around us daily\u2014whether we notice or not. What\u2019s exciting to see is how it\u2019s improving our quality of life, supporting quicker and more effective execution of some business operations and industries, and uncovering patterns that humans are likely to miss. Here are examples of machine learning at work in our daily life that provide value in many ways\u2014some large and some small. Facial recognition Facial recognition is one of the more obvious applications of machine learning. Targeted marketing with retail uses machine learning to group customers based on buying habits or demographic similarities, and by extrapolating what one person may want from someone else\u2019s purchases.\n\nTitle: What is the Future of Machine Learning? - Mission Cloud Services\nURL: https://www.missioncloud.com/blog/what-is-the-future-of-machine-learning\nContent: Machine learning (ML) is frequently confused with generative AI. This architecture allows deep learning models to extract features from raw data, making them effective for complex tasks such as image and speech recognition. Unlike traditional ML methods that often require manual feature extraction, DL can learn directly from unstructured data like images or text, enhancing its scalability and performance in handling large datasets. Enhanced AI Applications: The rise of multimodal machine learning enables systems to process diverse data types simultaneously, improving interaction capabilities. Reliable machine learning models depend on high-quality data. Mission\u0027s AI-powered governance and compliance features facilitate oversight of ML systems, helping mitigate data privacy and bias risks. Businesses can capitalize on machine learning by leveraging Mission\u2019s AI/ML expertise.\n\nTitle: AI and Machine Learning Trends in 2025 - DATAVERSITY\nURL: https://www.dataversity.net/ai-and-machine-learning-trends-in-2025/\nContent: Autonomous vehicles are expected to navigate more complex environments with unprecedented precision, thanks in part to sophisticated AI algorithms that can process vast amounts of data in real time. This influx of data provides unprecedented opportunities for ML to learn and adapt with more precision and real-time insights, paving the way for innovations in predictive analytics, automation, and user personalization. AI and ML empower e-commerce platforms to continue to enhance the personalized shopping experiences by analyzing vast amounts of customer data. As the volume of biological data grows exponentially, AI and ML are becoming indispensable tools for managing and interpreting this complex information. By analyzing vast amounts of educational data, AI can predict students\u2019 learning trajectories, thereby implementing timely interventions to help struggling learners.",
"type": "text"
}
],
"status": "success",
"tool_name": "tavily-search"
},
"duration_seconds": 5.281751,
"end_time": "2025-07-07T10:50:44.358915",
"message_sent": true,
"start_time": "2025-07-07T10:50:39.077164",
"timestamp": "2025-07-07T10:50:44.358915",
"worker_executed": true,
"workers_notified": true
},
"output": "Task completed successfully",
"result": [
{
"annotations": null,
"text": "Detailed Results:\n\nTitle: Real-World Examples of Machine Learning (ML) - Tableau\nURL: https://www.tableau.com/learn/articles/machine-learning-examples\nContent: As more organizations and people rely on machine learning models to manage growing volumes of data, instances of machine learning are occurring in front of and around us daily\u2014whether we notice or not. What\u2019s exciting to see is how it\u2019s improving our quality of life, supporting quicker and more effective execution of some business operations and industries, and uncovering patterns that humans are likely to miss. Here are examples of machine learning at work in our daily life that provide value in many ways\u2014some large and some small. Facial recognition Facial recognition is one of the more obvious applications of machine learning. Targeted marketing with retail uses machine learning to group customers based on buying habits or demographic similarities, and by extrapolating what one person may want from someone else\u2019s purchases.\n\nTitle: What is the Future of Machine Learning? - Mission Cloud Services\nURL: https://www.missioncloud.com/blog/what-is-the-future-of-machine-learning\nContent: Machine learning (ML) is frequently confused with generative AI. This architecture allows deep learning models to extract features from raw data, making them effective for complex tasks such as image and speech recognition. Unlike traditional ML methods that often require manual feature extraction, DL can learn directly from unstructured data like images or text, enhancing its scalability and performance in handling large datasets. Enhanced AI Applications: The rise of multimodal machine learning enables systems to process diverse data types simultaneously, improving interaction capabilities. Reliable machine learning models depend on high-quality data. Mission\u0027s AI-powered governance and compliance features facilitate oversight of ML systems, helping mitigate data privacy and bias risks. Businesses can capitalize on machine learning by leveraging Mission\u2019s AI/ML expertise.\n\nTitle: AI and Machine Learning Trends in 2025 - DATAVERSITY\nURL: https://www.dataversity.net/ai-and-machine-learning-trends-in-2025/\nContent: Autonomous vehicles are expected to navigate more complex environments with unprecedented precision, thanks in part to sophisticated AI algorithms that can process vast amounts of data in real time. This influx of data provides unprecedented opportunities for ML to learn and adapt with more precision and real-time insights, paving the way for innovations in predictive analytics, automation, and user personalization. AI and ML empower e-commerce platforms to continue to enhance the personalized shopping experiences by analyzing vast amounts of customer data. As the volume of biological data grows exponentially, AI and ML are becoming indispensable tools for managing and interpreting this complex information. By analyzing vast amounts of educational data, AI can predict students\u2019 learning trajectories, thereby implementing timely interventions to help struggling learners.",
"type": "text"
}
],
"return_code": 0,
"status": "success",
"stderr": "",
"task_id": "search_tavily",
"tool_name": "tavily-search"
}
}
}