BIOMEDANA AGENTS / PHARMA & BIOTECH
BioMedAna Agents brings 100+ ready agents to evidence-heavy bioprocess work. It connects approved sources, breaks requests into steps, and coordinates specialist agents across development, MSAT, manufacturing, quality and CMC. Evidence-linked findings and drafts go to your experts for review and decision.
01 / THE OFFER
Start with an available agent, then shape a BioMedAna workflow around your scientific question. We define the task with your scientific and digital teams, connect only the sources it needs, and design an output that a named expert can review. A ready agent does not mean a customer-specific regulated workflow is already validated; integrations, controls and intended use are evaluated for each engagement.
02 / ORCHESTRATION
The agent-level MoE architecture brings together the specialist agents a question needs—not the whole library. An internal supervisor checks their evidence and loops gaps back for another pass. Only then does a cited brief reach the human decision owner.
Router idle
Media lot changed before run 14
DO dips after feed 3 in affected runs
Titer −11% vs. baseline; glycans stable
Open deviation on feed pump PM
A scientist selects the program, runs, systems and reviewable output before any agent receives a task.
03 / SIX WORKFLOW CATEGORIES
Six ways to put ready agents to work across pharma and biotech. Data readiness is part of process knowledge, not a standalone category. Each workflow is scoped and evaluated with your team.
Locate prior runs, align their context and flag missing or inconsistent source data before it is reused in analysis. Keep mappings and conclusions linked to the original records.
Compare prior experiments and process conditions, surface gaps in the evidence and prepare options for the next study. Scientists choose and approve any experiment.
Compare process versions, scale changes and site-specific operating context. Flag missing evidence before the transfer or comparability package moves to technical review.
Bring batch records, trends, events and assays into one investigation trail. Surface differences and draft questions; the team determines cause and disposition.
Assemble the affected records, procedures and process context for a deviation or proposed change. Prepare an impact checklist for Quality to assess and approve.
Find approved reports, methods and results; organize them by the question being answered; and prepare a citation-ready evidence pack for qualified reviewers.
04 / WHAT A WORKFLOW PRODUCES
An illustrative batch-investigation flow shows how an agent can organize evidence without making a quality or scientific decision on its own.
Compare the affected run with relevant prior runs and surface source records for expert investigation.
Time-aligned operating conditions
Feed, sampling and interventions
Titer and quality measurements
Every finding must point back to an accessible source record.
What changed across the selected runs, with citations.
Missing data, confounders and alternative explanations.
Suggested checks—not an automated root-cause verdict.
Conceptual workflow visual. Actual data sources, calculations, access rules and review steps are defined and validated per engagement.
05 / BUILT FOR ACCOUNTABILITY
Scientific and regulated work needs more than a fluent answer. Each engagement defines what an agent can access, produce and change—and what remains a human decision.
Results show the underlying records and distinguish retrieved evidence from generated interpretation.
Connect only approved systems and actions. Start read-only where possible, and require explicit approval for consequential writes or externally used outputs.
Missing evidence and low-confidence results must be visible. An agent should stop or escalate when the task exceeds its defined boundary.
Test accuracy, citations, omissions, reviewer effort and failure cases against an agreed baseline before wider use.
06 / AGENTIC AIDLC
Our agentic AI development lifecycle (AIDLC) takes a workflow from a scientific question to supervised use. Ready agents accelerate assembly; each customer’s data, controls and intended use still determine the validation work.
Choose one decision, its human owner, baseline process, source systems and success measures.
Map specialist roles, task handoffs, allowed tools, evidence requirements and escalation rules.
Configure ready agents and scoped integrations; keep permissions and source lineage visible.
Test representative and failure cases for accuracy, citations, omissions and reviewer effort.
Begin supervised use, require approval at consequential gates and record what was reviewed.
Track drift, exceptions and feedback; re-evaluate changes before expanding the workflow.
Hub connects and governs bioprocess evidence; Twin supports predictive and simulation workflows. BioMedAna Agents adds customer-specific, multi-step work around that evidence and other approved systems. Each Agents workflow is scoped separately, and an engagement does not require every BioMedAna product.
START WITH A WORKFLOW
Bring a task, its owner and the evidence it depends on. We’ll discuss a bounded BioMedAna Agents pilot and how to judge it.
Talk to BioMedAna