Jobs

Run long-running computational pipelines autonomously in the background.

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:

1. Planning — The agent reads your instructions, inspects available data, and creates a step-by-step execution plan.
2. Execution — Each step runs sequentially: installing packages, writing scripts, processing data, generating outputs. You can see progress in the job panel.
3. File sync — Output files are uploaded directly to cloud storage as they are produced. No data passes through the web server.
4. 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.