Agentic LIMS Delivery
Clearline Tech Methods · applied AI + laboratory operations
I don't treat a LIMS engagement as a prompt that produces software. I treat it as a delivery system: accepted requirements, bounded work, an assembled candidate, and evidence that a human can use to make a release decision.
These are architecture and delivery-method notes, grounded in my September 3, 2026 field report. They are not a customer case study, a compliance certification, or a benchmark of a particular installation.
Validation map: define what “works” means
A sample-receipt requirement needs more than a screen. It needs the intended state, required fields, permitted transitions, resulting records, and a test that demonstrates them. A report needs its source data and comparison method. A migration needs mappings, transformations, exceptions, and reconciliation conditions.
I map each accepted requirement to those artifacts and its expected evidence. That makes dependencies visible before a lane implements its own interpretation. Unsettled laboratory terminology goes back to the process owner; it doesn't become a confident guess in a field name.
One writer per artifact, several delivery lanes
The system separates work by artifact and dependency rather than giving several agents the same engagement to edit:
- Workflow and configuration: states, fields, roles, rules, and outputs.
- Migration and data: source profiling, target mappings, rejected cases, and reconciliation.
- Infrastructure and release: the environment, deployment procedure, backups, and return path.
- Documentation and validation: executable cases, operating procedures, and traceable results.
Each shared configuration, mapping, procedure, or validation record has one writer. Other lanes can inspect it and raise discrepancies, but its owner integrates the authoritative version. The tradeoff is deliberate: a little coordination now instead of reconciling two plausible implementations of different laboratory meanings later.
Native workflow fit before custom code
I first test whether existing workflow, permissions, calculations, configuration, and reporting surfaces express the requirement faithfully. Native fit is not a reason to change the laboratory's meaning to suit the software. It is a way to avoid adding custom code that has to be validated, released, explained, and maintained without improving the result.
When there is a real gap, I bound it. A transition might depend on information the native workflow cannot express; an output might need an unavailable transformation. That gap becomes the custom implementation—not a justification to replace the surrounding process.
Disposable clone: evaluate the assembled behavior
Lane-level checks don't establish that the system works together. I assemble a production-like clone from the intended configuration, representative approved data, and the intended release procedure. The checks follow complete paths: receive a record, move it through the workflow, generate the expected output, and confirm the resulting data.
The useful failures are often at the seams. Data can import successfully while violating a workflow assumption. A field can render while the intended role cannot update it. A report can look right while using the wrong reference value.
A failed clone can be discarded and rebuilt from maintained artifacts. Repairing it by hand until it looks healthy would leave a different question unanswered: can the candidate reproduce the result without undocumented corrections?
Release evidence is more than a successful deployment
I compare released configuration with the accepted candidate, reconcile migrated records and declared exceptions, and rerun representative workflows. Configuration identity, reconciliation, validation results, unresolved exceptions, and rollback or restoration material describe one delivered state and belong in the same release record.
The human boundary stays explicit. Agents can propose interpretations and prepare work; the laboratory owner resolves operational meaning. Faster execution does not grant a model authority to decide that two materials are equivalent or that a consequential exception is acceptable. Cleanup waits until the delivered state is accepted and the previous usable state is no longer needed for recovery.
Limitations and what the evidence does not say
The published report describes compression from months to days through overlapping work, repeatable rebuilds, and smaller custom gaps. It does not publish a controlled comparison, a per-installation timing dataset, or a universal schedule guarantee. I don't turn that experience claim into a measured multiplier here.
Missing source data, unclear laboratory practice, difficult transformations, and consequential exceptions still take the time they take. The delivery method can reduce serial handoffs; it cannot supply decisions that only the laboratory can make. A reproducible clone and traceable release record support review, but they do not by themselves establish scientific validity or regulatory approval.
Source and next step
Read the original field report →
This is the kind of principal/staff applied-AI work I want to keep doing: connect orchestration, evaluation, enterprise integration, and production operations without losing the person accountable for the result.