How to Use AI Agents in Biology: A Practical Guide

By Chirag Gupta, PhD · September 5, 2026 · 12 min read

An AI agent can take a count matrix, select an analysis path, run code, and return a biological interpretation before many researchers have finished configuring the software. That speed is useful, but it compresses dozens of scientific decisions into a conversation.

The essential skill of a biologist working with agents is not just being able to express their research goals in a perfect prompt, but it is also making the agent expose its assumptions, show its work, and distinguish evidence from interpretation.

This matters because an agent is more than a chatbot. A chatbot can explain differential expression in RNA-seq data, for example. An agent may inspect your files, choose a method, install software, set parameters, execute an analysis, examine the output, align it with literature,and decide what to do next. In other words, it can participate in the analysis rather than merely discuss it.

That makes AI agents genuinely useful in biology. They can take load off the scientist for the routine standardized analyses. It also means they should be treated like computational collaborators: capable of doing substantial work, but expected to document their decisions and support their conclusions.

I have been talking to biologists, bioinformaticians, and data scientists using Pipette as their research agent almost every week over the past six months. Talking to our users has given me a better perspective on how biologists approach an AI scientist, where they stumble, what they often overlook, and the things we developers need to improve in the agentic system itself to make their transition and integration as smooth and reliable as possible.

In this guide, I focus on agents used for computational analysis of biological data. Laboratory automation and autonomous experimentation require additional safeguards beyond those discussed here.

Start with the biological question, not the software

“Analyze my RNA-seq data using Deseq2” is not a sufficient scientific specification. It lets the agent infer the experimental design, the relevant contrast, the unit of replication, and even what a useful answer would look like.

Give the agent the same information you would give a bioinformatician joining the project:

• The organism, tissue, cell type, and experimental system
• Whether the data are raw or processed
• What each sample represents
• The biological conditions and comparisons of interest
• The number of biological and technical replicates
• Any pairing, repeated measurements, time points, blocks, batches, sites, or donors
• The reference genome, annotation release, or identifier system, if known
• The biological question the experiment was designed to answer
• The outputs you need and the decision they will inform

A useful prompt might look like this:

I have gene-level raw counts from 24 Arabidopsis leaf samples: two genotypes, control and drought treatment, with six biological replicates per group. The sample metadata include two sequencing batches, with each genotype-treatment group split evenly between them. Test the drought effect within each genotype and the genotype-by-treatment interaction. First audit the metadata, show the proposed design matrix, and flag any confounding. Preserve the raw counts, justify filtering decisions, report effect sizes and FDR-adjusted values, and save the code, software versions, and the intermediate results needed to audit the analysis. Stop and ask me if missing information could materially change the analysis.

This is much better than naming a favorite tool and asking the agent to run it. It tells the agent how the data were generated and what must be learned from them. The software should follow from that design.

What to look for after the agent completes a task

The quality of an agent is not defined by how polished its final report sounds. Look for evidence that it handled the scientific work correctly.

It inspects the data before choosing a workflow

The agent should confirm file formats, dimensions, sample names, feature identifiers, missing values, and metadata consistency before analysis. It should not silently infer treatment groups from filenames or assume that a processed expression table contains raw counts.

It should also count what enters and leaves each major stage: reads, samples, cells, genes, taxa, proteins, or variants, depending on the experiment. If those numbers are absent, it is difficult to tell whether a filtering or parsing error changed the dataset.

It makes consequential decisions visible

Many bioinformatics decisions are reasonable only in context. A mitochondrial count-fraction threshold suitable for one single-cell dataset may be inappropriate for another. A batch term may improve a model, while perfect confounding between batch and condition can make the intended biological contrast impossible to estimate.

The agent should identify choices that can change the result, explain why it made them, and show their consequences. Routine implementation details do not require an essay, but filtering thresholds, model design, reference resources, and sample exclusions deserve explicit justification.

It preserves provenance

A credible result should be traceable to its inputs. A practical analysis record should retain:

• Input identities or checksums and sample metadata
• Reference genome, annotation, and database versions
• Commands, code, parameters, and software versions
• Filtering decisions and excluded samples
• Warnings, logs, final outputs, and the intermediate files needed to audit or restart the analysis
• Random seeds for stochastic steps

This is not paperwork added after the science. It is what allows another scientist to reproduce, audit, or extend the work.

It is appropriate for the data you are allowed to share

For human, clinical, or controlled-access data, scientific quality is only one requirement. Confirm where the data will be processed, which service providers receive them, how they are encrypted, how long they are retained, who can access them, and whether data or prompts are used for model training. Check that the intended use is permitted by the consent, data-use agreement, institutional policy, and any applicable controlled-data or clinical requirements. Do not upload sensitive data to a general-purpose assistant merely because the interface accepts files.

It can report uncertainty and failure

An agent should NOT be compelled to produce a positive biological story from every dataset. Sometimes the groups are confounded, the replication is insufficient, a reference is incompatible, or the signal does not survive correction for multiple testing. Reporting that clearly is a successful analytical outcome.

Be cautious when every run ends with a confident mechanism, a clean set of pathways, and no discussion of ambiguity.

How to check scientific integrity of an agent

Scientific integrity here does not mean detecting deliberate misconduct. It means checking whether the path from data to claim is valid, visible, and proportionate to the evidence.

1. Verify the identity of the data

Before examining a volcano plot or pathway table, establish that the correct data were analyzed.

Check whether samples were assigned to the right conditions, identifiers were mapped to the correct organism and database, and the annotation matched the genome build. Confirm that matrix orientation and units were interpreted correctly, paired-end reads remained paired, and duplicate identifiers or technical replicates were handled explicitly.

A sophisticated downstream model cannot rescue an incorrect sample sheet or a mismatched reference genome.

2. Confirm that the statistical model reflects the experiment

Ask to see the sample-to-condition table and the design matrix. The statistical unit must match the experimental unit.

Technical replicates are not independent biological replicates. Thousands of cells from one donor do not create thousands of independent donors. Paired and longitudinal measurements should not be modeled as unrelated samples. A condition cannot be cleanly separated from a batch when the two are perfectly confounded.

These errors often produce plausible figures. That is why the design must be inspected directly rather than inferred from the appearance of the output.

3. Examine quality control before accepting exclusions

Quality-control thresholds should be accompanied by distributions, counts, and reasons. Ask how many samples or features were removed at each step and why a threshold was appropriate for this dataset.

An outlier should not be removed simply because it weakens the expected result. If an exclusion is debatable, request the analysis both with and without that sample. A conclusion that depends entirely on one borderline decision should be presented as sensitive, not robust.

The relevant diagnostics vary by assay. RNA-seq may require library size, mapping, duplication, and sample-level structure. Single-cell analysis requires attention to detected features, total counts, mitochondrial signal, doublets, ambient RNA, and sample composition. Microbiome, proteomics, and variant workflows each have their own failure modes. “QC passed” is not enough; the evidence should be visible.

4. Inspect the statistics, not only the ranked list

Ask for effect sizes and uncertainty, not only P-values. Confirm that multiple testing was addressed and that the input scale was appropriate for the method. For example, a count-based differential-expression model generally expects raw counts rather than TPM values.

Check whether important covariates were included, whether the available sample size supports that model, and whether diagnostics were examined. In machine-learning analyses, confirm that related measurements from the same patient, genotype, field, or study did not appear in both training and test sets. Leakage can make a weak model look excellent.

5. Separate observation from interpretation

An AI agent is very good at constructing a coherent biological explanation. Coherence, however, is not validation.

Differential expression does not establish a mechanism. Pathway enrichment does not prove that a pathway is activated. A predicted interaction is not an experimentally demonstrated interaction. A cluster label is an annotation supported by markers, not a directly measured cell identity. Feature importance does not by itself establish causality.

Ask the agent to label what was directly observed, what is consistent with prior knowledge, what remains speculative, and what alternative explanations fit the result. Literature citations should also be checked: the paper should exist, and it should support the specific claim for which it was cited.

6. Test whether the conclusion is robust

Not every analysis needs several competing pipelines. But important conclusions should survive reasonable changes in assumptions.

Useful checks include varying a borderline QC threshold, repeating the analysis with and without an outlier, testing a plausible alternative model, or confirming a key result with an orthogonal method. The goal is not to search until the desired result appears. It is to learn which findings are stable and which depend on one analytical choice.

Follow-up questions that make the analysis better

The first answer from an AI agent should be the beginning of scientific review, not the end. Good follow-up questions force the important decisions into view.

Before execution, ask:

• “What information is missing that could change the analysis?”
• “Show me how you interpreted the experimental design.”
• “Which comparisons are statistically estimable from these samples?”

During quality control, ask:

• “How many samples and features remain after each filter?”
• “Could batch, donor, site, or sequencing depth explain this separation?”
• “Show the result with and without the questionable samples.”

After modeling, ask:

• “What is the independent unit of replication, and which covariates are in the model?”
• “Report effect sizes, uncertainty, and multiple-testing correction—not only raw P-values.”
• “Which reasonable analysis choices would change the main result?”
• “Trace each main conclusion to the exact table, plot, or output file that supports it.”

During interpretation, ask:

• “Which claims come directly from my data, and which are literature-based interpretations?”
• “What is the strongest alternative explanation, and what cannot be concluded from this experiment?”
• “Which finding is most robust, and which is most uncertain?”
• “What experiment would best validate this result?”

You do not need to ask every question after every analysis. Focus on the decisions most likely to change the biological conclusion.

Warning signs

Pause and investigate if an agent produces:

• A polished interpretation without showing sample counts or intermediate results
• Silent filtering, sample exclusions, or parameter changes
• A design that ignores donors, pairing, batches, or repeated measurements
• Claims based on nominal significance without multiple-testing correction
• Pathway or mechanistic conclusions unsupported by the underlying effect sizes
• Citations that are missing, incorrect, or irrelevant to the claim
• Repeated reruns with no clear diagnostic reason
• A report that cannot be connected back to executable code and retained outputs

The common feature is not necessarily that the conclusion is wrong. It is that you cannot determine whether it is right.

How Pipette approaches scientific quality

Pipette is built on the same premise: an agent’s output should be evaluated like any other computational analysis. Workflows include data-type-specific QC, checks on intermediate results, and review of whether the conclusions are supported by the evidence. Commands, outputs, and analytical decisions remain visible so a scientist can inspect what happened rather than accept a polished summary on trust. When the data do not support a confident conclusion, the system should say so.

We treat analytical skills as maintained, versioned scientific methods rather than static prompts. We review and update them as the underlying software, databases, and recommended practices change. Before adding a new workflow, we test its important decisions, expected intermediate checks, failure modes, and outputs on representative datasets. A workflow can continue to run after its assumptions have become outdated, so successful execution alone is not enough.

Cost is also part of workflow design. Our goal is to produce a scientifically defensible analysis with the minimum practical dollar spend. That does not mean making an analysis artificially cheap by removing necessary checks. That means matching computational resources to the dataset, avoiding unnecessary reruns, reusing completed work when appropriate, and reserving more expensive reasoning or compute for the parts of an analysis that genuinely require it.

Pipette does not remove the scientist’s responsibility to review an analysis. It is designed to make that review possible: to expose the work, retain the evidence, and support informed follow-up questions.

Shared workspaces preserve scientific knowledge

Biological analysis is rarely the work of one person or one afternoon. A useful result includes the files, decisions, questions, failed attempts, and follow-up analyses that produced it.

Pipette’s shared workspaces keep the files, analyses, and discussions created in Pipette with the project, where the research team can inspect them and build on them. A collaborator can see not only the final figure, but how it was produced, why a sample was excluded, which alternative was tested, and what remained unresolved.

Shared workspaces are therefore more than a collaboration feature. They make scientific review and quality control easier to perform. The goal is to to preserve the knowledge required for the next scientist, and the same lab six months later, to understand, question, and extend it.