Build data-analysis agents on Polyaxon

A data-analysis agent is useful when it can turn a question into an analysis that another engineer can inspect. A plausible paragraph is not enough. The result needs a dataset version, executable reasoning, and checks that establish whether the calculation answered the question.

Polyaxon can run the preparation, execution, and evaluation stages together. Keep the agent's exploratory behavior inside an explicit analysis contract, then promote useful discoveries into repeatable jobs.

Give the agent a bounded question

Suppose an ML team wants to understand why evaluation failures increased after a dataset refresh. Give the agent a sanitized table containing run identifiers, dataset revisions, model versions, failure categories, and evaluation timestamps.

Specify the comparison window and the denominator. A rise in failed runs can reflect more total runs rather than a worse failure rate. The agent should distinguish those possibilities before suggesting a root cause.

An analysis contract might require:

• Counts and failure rates for each dataset revision.
• A separate result for incomplete or missing evaluations.
• The query or script used to calculate each table.
• A statement of uncertainty where sample sizes are small.
• No modifications to source data or production models.

These are application requirements, not behavior that an LLM automatically guarantees.

Separate retrieval from generated execution

Run an approved collector as a Polyaxon job with the required data connection. Produce a versioned, minimized analysis bundle. The generated program should consume that bundle rather than receive a broad warehouse credential.

For an interactive investigation, use a sandbox-enabled service. The controller can upload the bundle, execute a program, inspect a report, and ask the model to revise its approach. Configure the service image with reviewed analysis dependencies and appropriate CPU and memory limits.

For a fixed recurring report, prefer a job. Once the question and calculation stabilize, there is little value in asking a model to regenerate the same aggregation every morning.

Make numerical verification independent

Do not ask the same agent to certify its own answer. Use a trusted evaluator to check row counts, allowed categories, duplicate identifiers, missing values, and reconciliation against the input totals.

For example, a report that groups 1,000 evaluation records should account for all 1,000 records, including unknown outcomes. A chart that silently drops unsuccessful requests can make reliability look better than it is.

Use tracking metrics for the evaluator's numerical results and artifacts for the detailed report, generated program, and validation findings. Keep confidential row-level data out of broadly visible metadata.

Passing structural checks does not prove causality. If the data shows an association between a new dataset and failures, label it as an association and propose a controlled follow-up experiment.

Turn successful investigations into reusable work

Review the generated code before placing it in the team's repository. Pin dependencies, parameterize the dataset revision, and package the approved analysis as a versioned component.

Future runs can compare a candidate analysis against the same reference cases. Include deliberately incomplete records and changed schemas so the workflow fails clearly when assumptions no longer hold.

The result is an agent-assisted analysis process with a stable operational foundation: Polyaxon executes and tracks the work, the model helps explore, and an independent evaluation step decides whether the evidence is ready to share.