Seqera AI is the bioinformatics agent purpose-built to support scientists throughout the R&D lifecycle, from answering complex questions and generating pipeline code to analyzing results with high accuracy. Since launch, thousands of scientists have leveraged Seqera AI to accelerate Nextflow development, troubleshooting existing pipelines, generating entirely new pipelines, and developing directly in the VS Code extension.
Today, we're excited to announce that Seqera AI now has read and write access to Seqera Platform. This enables Seqera AI to directly interact with Seqera Platform primitives to debug pipelines, modify parameters, and launch runs, all without switching context between different tools and interfaces.
Bridging the Gap Between AI and Execution
One of the most common pieces of feedback we've received from users is: "The code generation is excellent, but what if Seqera AI could actually run the pipeline on my data?" This new integration addresses exactly this need, creating a unified experience where pipeline development, debugging, and execution happen seamlessly within a single AI-powered interface. With Platform read and write access, Seqera AI can now:
- Debug failed runs by pulling workflow logs and identifying specific failure points
- Modify pipeline parameters and relaunch runs with updated configurations
- Launch pipelines directly from Launchpad without leaving the chat interface
- Browse S3 bucket structures to understand data organization and availability
Seqera AI now has direct access to your compute environments, your data, and your execution history, enabling true iterative development and debugging.This represents a key shift from traditional AI tools that operate in isolation.
4 Things You Can Now Do With Seqera AI
1. Debug Failed Runs
When a workflow fails in Platform, Seqera AI can immediately pull execution logs, pinpoint the exact failure point, and explain what went wrong in plain language. Instead of manually digging through complex log files, simply ask "What went wrong with this run?" and get actionable insights with suggested parameter fixes and the option to relaunch immediately.
2. Modify Pipeline Parameters and Relaunch
Resource allocation issues become trivial to resolve. If a pipeline consistently fails due to memory constraints, Seqera AI analyzes your run history, identifies the bottleneck, and suggests optimal resource settings. Seqera AI can then immediately relaunch your workflow with the updated configuration, turning hours of trial-and-error into a single conversation.
3. Launch Pipelines from Launchpad
Seqera AI can directly launch any pipeline available in your Launchpad without switching interfaces. Whether you're running a standard nf-core pipeline or a custom workflow, simply describe what you want to run and Seqera AI will handle the configuration and execution, streamlining the path from idea to analysis to results.
4. Browse S3 Bucket Structures
For researchers with datasets stored in S3, Seqera AI can explore your bucket structure, understand data organization, and identify available files without requiring manual navigation. This data-aware capability helps match your data to appropriate pipelines and ensures proper input configuration.
What This Means for Your Research
This Platform integration eliminates context-switching between AI-assistance and execution, enabling continuous pipeline development, testing, and optimization in a single conversation. Researchers can describe their data and analysis goals in natural language while Seqera AI handles the technical implementation and execution details. This creates a unified experience where AI serves as an intelligent bridge between scientific objectives and computational execution, dramatically accelerating the path from experimental data to actionable insights.
Looking Ahead
The integration of Seqera AI and Seqera Platform represents just the beginning of a more connected, intelligent research experience. As we continue expanding these capabilities, our vision is clear: seamless, AI-powered research workflows that understand your data, your compute resources, and your scientific objectives, enabling you to focus on discovery rather than implementation.