Multi-agent pipelines for clinical data analysis with LangGraph

"Throw a large language model at it" is a tempting first pass for clinical data analysis. It almost always produces something that looks right and is quietly wrong. This post is about why single-LLM pipelines fail in regulated data work, and how a LangGraph multi-agent design solves the specific failure modes.

Why single-LLM pipelines fail for clinical data

The honest answer: LLMs are good at producing fluent output, not defensible output. For clinical data the difference is the entire job. Five concrete failure patterns we see again and again:

1. Silent schema assumption. The LLM reads the first 10 rows, infers columns, and silently miscasts a date column as a string. Downstream calculations are wrong and the report reads confidently.
2. Test selection on autopilot. Model picks a t-test for non-normal data because the prompt asked for "the standard test." No normality check, no non-parametric alternative.
3. No multiple-comparisons correction. 40 biomarkers, 40 p-values, none adjusted. This is a textbook auditor flag.
4. Hallucinated statistics. Model reports an r² of 0.87 when it never actually ran the regression — it inferred a plausible value from context. Rare, catastrophic.
5. No audit trail. The entire pipeline is a single prompt, so you cannot answer "why did the test change between run 12 and run 13?" — because the run is undifferentiated.

What "multi-agent" actually means

Multi-agent here is not marketing. It means the analysis is decomposed into explicit stages, each with a single responsibility, a typed input and output, and a well-defined failure mode. Some stages use an LLM, some are pure Python. Orchestration is a directed graph.

LangGraph is a good fit because it models the pipeline as a state graph — nodes are stages, edges can be conditional, and state is typed. This gives you three properties that matter:

• Each stage is independently testable.
• Stage boundaries are obvious — easy to insert human review gates.
• Streaming state updates are first-class, which matters for UI progress and audit trails.

A concrete 22-stage design

Here is the shape of the pipeline AiLabrix uses. Each name is a LangGraph node.

data-intake          → profile schema, detect assay type
assay-context        → contextualize columns (e.g. ELISA vs qPCR)
preprocessing        → missing values, encoding, assay-specific imputation
eda                  → distributions, outliers, correlations
figure-qa            → vision model checks chart readability
data-ops-review      → HUMAN GATE: data quality review
method-validation    → choose statistical methods for the data
insufficient-data-halt → conditional: stop if n too small
statistical-analysis → run the methods, compute results
scientific-interpretation → LLM interprets findings in context
scientific-critique  → LLM peer reviews the interpretation
scientific-ops-review → HUMAN GATE: scientific review
literature-evidence  → LLM pulls published context
knowledge-review     → LLM validates the literature match
clinical-relevance   → LLM assesses clinical significance
regulatory-review    → check compliance (CLIA, ISO 15189, CLSI)
traceability         → build the audit matrix
technical-reporting  → generate the PDF
qa-documentation     → assemble the QA package
qa-review            → final verification
program-review       → program management sign-off
executive-brief      → LLM executive summary

Why 22 and not 5 or 100

Not a magic number. The test is: can this stage fail for a reason that is different from the previous stage? If yes, it is its own node. If no, merge. Twenty-two came from working backwards from the set of errors we wanted to catch — not from wanting 22 boxes on a slide.

Three LangGraph patterns that pay for themselves

1. Typed state with explicit fields

class PipelineState(TypedDict):
    job_id: str
    input_file: str
    work_dir: str
    stage_log: list[StageLogEntry]
    stage_results: dict[str, Any]
    errors: list[str]
    halt_reason: str
    gate_pending: str
    critique_iter: int
    critique_has_flaws: bool

Every node reads and writes a subset of these fields. No hidden state. Debugging becomes: print state at the failing node.

2. Human-in-the-loop gates

Two of the 22 stages are human gates: data-ops-review and scientific-ops-review. The LangGraph node blocks on a database poll until the gate is approved, rejected, or revision is requested. This is how you get clinical oversight without a messy branch-and-callback architecture.

def node_data_ops_review(state):
    # ... generate the review artifact ...
    decision = human_gate_wait(state, "data-ops-review")
    if decision == "revision":
        # re-enter preprocessing with reviewer notes in state
        return {**state, "stage": "preprocessing", "revision_notes": ...}
    return {**state, "stage": "method-validation"}

3. Critique-revise loops for LLM stages

Any LLM stage that produces a judgment (interpretation, clinical relevance) is wrapped in a critique-revise loop: a second LLM peer-reviews the first, and revisions run until the critic agrees or a max iteration cap is hit. This catches the "confidently wrong" failure mode from single-pass generation.

What you get on the other side

• Reproducibility. Same data, same pipeline, same version — same report. Numbers to the last digit.
• Traceability. Every number in the PDF has a path back through specific nodes.
• Graceful failure. A single bad stage does not corrupt the whole run — it surfaces with a clear error.
• Extensibility. New assay type? Add a node, not a fork.

When this is overkill

If you are running exploratory analysis on a dataset you own and will never share, a Jupyter notebook is the right tool. Multi-agent pipelines earn their complexity when the output has to be defended — to an auditor, a regulator, a collaborator who will re-run it in six months.

Try it

AiLabrix is the reference implementation of the design above. Drop in a CSV, pick a template, get a signed PDF report out. Self-hosted on your infrastructure. [email protected] if you want a walkthrough.