- ReAct-based Multi-Agent Orchestration: The core Supervisor Agent interprets user's natural language queries and uploaded files to establish a high-level analysis plan, and selectively invokes specialized Sub-Agents to execute the analysis. Even without bioinformatics expertise, users can initiate complex pipelines using everyday language, such as "Perform differential expression analysis on this RNA-seq data." - ExecutorAgent: Autonomous Code Generation, Execution, and Debugging: Once the analysis plan is finalized, the ExecutorAgent automatically generates and executes task-specific code, and performs self-debugging in case of errors. Users receive only the results without coding. - Dynamic Docker Image Selection (Sandbox Execution): Automatically selects and deploys Docker images suitable for the analysis type from container registries such as Docker Hub, Quay.io, and Wave. All analyses are executed in an isolated sandbox environment, ensuring both reproducibility and security. - Dual-Layer Scalability: The Sub-Agent layer and the Tool layer can be scaled independently. New bioinformatics tools or analysis pipelines can be added as plugins, allowing for customized analyses beyond standard workflows. - Chief Scientific Orchestrator (CSO): A meta-model at the top level of the PromptBio platform, which interprets user intent and orchestrates domain-specific agents such as literature mining, multi-omics integration, protein engineering, and drug discovery. - Graph-Based Workflow Engine: Models dependencies between analysis steps as a graph, managing pipelines in a DAG (Directed Acyclic Graph) manner, where subsequent steps are executed only after the preceding steps are completed. - No-Code to Full-Code: Supports a flexible dual approach, allowing non-experts to perform analyses immediately through an interactive interface, while experts can modify and extend the generated code.