Eigent's Intelligent Body Collaboration uses a task decomposition-assignment-aggregation workflow. Take the example of generating a Q2 financial report:
- Task Creation: User input
eigent create-task --name "q2-report" --description "Generate Q2 financial report from CSV"The system analyzes the requirements and automatically breaks them down into sub-tasks such as data cleansing, metrics calculation, and format generation. - intelligent allocation of resources::
- developer intelligence: Responsible for extracting data from CSV and calculating metrics such as ROI, possibly calling Python pandas library
- Documentation Intelligence: Conversion of structured data into accounting standards-compliant report templates, handling layout and visualization
- calibrated smartphone: Automatically check the consistency of data and trigger a manual review process if anomalies are found.
- parallel execution: Each intelligence processes the module it belongs to at the same time and exchanges intermediate results through a memory sharing mechanism. For example, the developer intelligence releases data to the document intelligence immediately after completing the computation.
- Result aggregation: The system automatically integrates the output of the report in PDF/PPT format and saves it to the
output/catalog or push directly to an integration platform (e.g. Notion).
The process is 3-5 times more efficient than traditional single-threaded processing and guarantees the quality of the output through the specialized division of labor among typed intelligences.
This answer comes from the articleEigent: an open source desktop application for automated multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " How does Eigent's Intelligent Body Collaboration Mechanism work in real-world tasks? Illustrated by the example of financial reporting biochemistry
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