Seqera Co-Scientist, released by Seqera Labs on April 30, 2026, is an AI-powered scientific research assistant that helps users build, run, and monitor complex next-generation sequencing and large-scale bioinformatics workflows using natural language. Just as developers benefit from an intelligent coding partner that translates plain-language instructions into precise code, this tool is designed to completely eliminate the programming barrier for bioinformaticians when developing and running Nextflow-based pipelines. Users can dynamically adjust and execute structured workflow pipelines through natural language conversations via the Seqera CLI and web interface, without having to worry about complex syntax or infrastructure setup. In traditional bioinformatics research environments, there is a technical burden of manually writing complex Nextflow scripts to analyze large-scale omics data and manually analyzing vast error logs whenever errors occur. To overcome these limitations, this tool provides a language model-based knowledge engine specialized in Nextflow and nf-core pipeline architectures, automating the entire process from pipeline auto-design to error recovery. While general AI tools are limited to interpreting the syntax of common code, this tool differentiates itself by incorporating domain-specific knowledge in the life sciences, allowing it to directly read logs and debug resource allocation or source code bugs in the execution path when an analysis pipeline is interrupted, thereby resolving errors. In real-world research settings, when a biotechnology researcher processes large-scale genomic source data, they can establish efficient workflows without manually tuning complex pipeline parameters or environment configurations. After a researcher executes a specific command in the CLI environment to complete the connection between the local development environment and the platform, and then requests that a specific genomic mapping engine and quality control tool be linked together using natural language, this tool instantly searches a database of over 1,000 validated nf-core modules in real time and presents the optimal pipeline framework. This process has the same effect as having an experienced bioinformatician directly refine the code, and it also automatically generates cloud resource scaling settings to optimize the execution cost and efficiency of the workflow. Even if unexpected exceptions such as memory overflow errors or missing file paths occur during execution, the autonomous agent precisely analyzes the execution logs of the interrupted task and initiates a self-healing process that either retries the task with appropriate computing specifications or modifies the code. Furthermore, through the web portal dashboard, non-developer research staff can easily execute published analysis pipelines under secure governance control and view visualized quantitative result graphs without complex code modifications. As a result, this tool reduces the burden of complex infrastructure management, providing the optimal environment for genomic research organizations to focus solely on discovering ultimate biological insights rather than data preprocessing or pipeline debugging.