Jobs are long-running computational tasks that run autonomously in a dedicated cloud sandbox. Unlike inline code execution — which runs a single code block and returns results immediately — jobs are designed for multi-step pipelines that can take minutes to hours: RNA-seq analysis, MD simulations, genome assembly, multi-file processing, and other workflows that require sustained compute.
You start a job from chat. MIP spins up a sandbox, plans the work, and executes it step by step. You can monitor progress, send follow-up instructions, pause, resume, or cancel at any time. When the job finishes, output files are saved to your Files library.
Starting a job from chat
Ask MIP to run something that requires extended computation. Be specific about what you want done, what data to use, and what outputs you expect.
Examples:
- “Run differential expression analysis on my RNA-seq count matrix using DESeq2. Compare treated vs control groups. Output a results CSV and a volcano plot.”
- “Set up and run a 10ns MD simulation of PDB 1UBQ using OpenMM. Save the trajectory and energy plots.”
- “Process all FASTA files in my uploaded dataset — run BLAST against nr, filter hits with E-value < 1e-10, and generate a summary table.”
MIP will create a background job, plan the steps, and begin execution. A job panel opens in your chat showing real-time progress.
Include specific parameters, file references, and expected outputs in your request. The job agent works autonomously — the more context you provide upfront, the better the results.
How a job runs
Once triggered, the job follows this lifecycle:
- Planning — The agent reads your instructions, inspects available data, and creates a step-by-step execution plan.
- Execution — Each step runs sequentially: installing packages, writing scripts, processing data, generating outputs. You can see progress in the job panel.
- File sync — Output files are uploaded directly to cloud storage as they are produced. No data passes through the web server.
- Completion — The job finishes, output files appear in your Files library, and you receive a notification.
What the job panel shows
While a job is running, the panel displays:
- Progress bar with current step and total steps
- Timeline of completed steps with outputs
- Plan showing the agent’s to-do list and completion status
- Messages for any follow-up communication between you and the agent
- Files produced so far, with download links
Sandbox environment
Each job runs in an isolated cloud sandbox with:
Pre-installed tools:
- Python 3 with pip
- R with development libraries
- Git, curl, jq, and standard build tools
- BWA, SAMtools, BCFtools for bioinformatics workflows
Pre-installed Python libraries:
- Core: NumPy, SciPy, pandas, matplotlib
- Bioinformatics: Biopython, openpyxl
- R integration via rpy2
Additional packages can be installed by the agent during execution using pip install or apt-get.
Data access:
- Your uploaded files are automatically available in the sandbox workspace
- The agent can download additional data from public sources as needed
Interacting with a running job
You are not limited to watching. While a job is running, you can:
| Action | How |
|---|
| Send a message | Type in the job panel to give the agent additional instructions or answer its questions |
| Pause | Suspend execution. The sandbox state is preserved — you can resume later. |
| Resume | Continue a paused job from where it left off. |
| Cancel | Stop the job permanently. Output files generated so far are still available. |
If the agent needs input from you — for example, to choose between analysis options — the job will show an input prompt. Respond in the message panel to continue execution.
Output files
All files produced by a job are automatically saved to your Files library. Common outputs include:
- Results tables (CSV, TSV)
- Plots and figures (PNG, SVG)
- Processed data files (H5AD, BED, FASTA)
- Scripts used during execution
- Execution logs
Files are uploaded directly from the sandbox to cloud storage — they never pass through the web application. Each file gets a time-limited download URL when you access it.
You can find job outputs on the Files page, where they appear with a “Generated by MIP” badge. You can also download them directly from the job panel.
Jobs vs code execution
| Inline code execution | Background jobs |
|---|
| Triggered by | Quick code requests in chat | Complex pipeline requests in chat |
| Execution | Single code block, runs immediately | Multi-step autonomous workflow |
| Duration | Seconds (30s max) | Minutes to hours (1 hour per session) |
| Results | Rendered inline in chat | Saved to Files library |
| Interaction | None — wait for result | Pause, resume, message, cancel |
| Best for | Quick plots, data exploration, small transforms | Pipelines, simulations, multi-file processing |
| State | Stateless — each execution is independent | Stateful — agent builds on previous steps |
| File size | Small outputs inline | Up to 5 GB per file |
Use inline code execution for quick tasks: “Plot the distribution of allele frequencies” or “Convert this CSV to JSON.” Use jobs for anything that requires multiple steps or will take more than a minute.
Limits
| Constraint | Limit |
|---|
| Max execution time per session | 1 hour (can pause and resume) |
| Concurrent jobs per user | 3 |
| Jobs per chat | 1 |
| Max file size per output | 5 GB |
Viewing all jobs
Navigate to Jobs in the sidebar to see all your background jobs. The page shows:
- Job title and status (running, paused, completed, failed, cancelled)
- Progress indicator (steps completed)
- Duration
- Creation date
Click any job to open its detail panel with the full timeline, messages, files, and execution plan. 