Multi-agent architecture
The virtual R&D department
The orchestrator decomposes a product goal into research questions, simulations, formulation tasks, experiments and decision gates — then routes each to a specialist.
Orchestration flow
PRODUCT / RESEARCH GOAL
│
AI R&D ORCHESTRATOR
│
┌─────┴─────┬────────────┐
Scientific Computational Product
Research Science Design
└─────┬─────┴────────────┘
▼
SAFETY ENGINE → REGULATORY ENGINE → LAB PLANNING
▼
EXPERIMENTAL RESULTS → LEARNING LOOP → OPTIMIZED COMMERCIAL PRODUCTScientific Research
Turns objectives into graded, traceable evidence.
Literature Agent
Papers, trials, meta-analyses → graded evidence summaries
Target Biology Agent
Condition → pathways, genes, proteins, biomarkers
Patent Agent
Prior art, claim families, freedom-to-operate risk
Evidence Agent
Level A–E classification and contradiction detection
Computational Science
The molecular discovery engine, shared across all three verticals.
Protein Structure Agent
PDB / UniProt / AlphaFold prep and validation
Molecular Screening Agent
Library filtering and virtual screening funnel
Docking Agent
Pose generation, multi-engine rescoring
Molecular Dynamics Agent
RMSD, RMSF, contact and H-bond persistence
ADMET Agent
Absorption, metabolism, clearance, hERG, permeability
Toxicology Agent
Mutagenicity, sensitization, organ toxicity signals
Medicinal Chemistry Agent
Analogue design and iterative optimization
Product Design
Converts candidates into manufacturable, stable formulas.
Formulation Agent
Phase-structured formulas with process notes
Compatibility Agent
Ingredient interaction graph and incompatibility flags
Stability Agent
Degradation prediction and stability protocol design
Preservative Agent
Water activity, pH and coverage-based preservation
Dose Optimization Agent
Effective dose vs. feasibility and upper limits
Combination Optimization Agent
Synergy, redundancy, antagonism ranking
Skin Penetration Agent
Stratum corneum permeability and retention
Validation & Compliance
Keeps every recommendation auditable and human-approved.
DOE Agent
Factorial, response-surface and mixture designs
Experiment Planning Agent
Protocols, batch sizes, pass/fail criteria
Lab Results Agent
Prediction error tracking and model recalibration
Regulatory Agent
Region-specific limits and dossier assembly
Claims Compliance Agent
Claim wording risk before packaging
Product Classification Agent
Cosmetic vs. medicinal boundary detection
Audit & Traceability Agent
Input, model, version, approval chain of custody
Commercial
Makes R&D decisions commercially defensible.
Supplier Agent
Grades, MOQ, lead time, COA history
Cost Agent
COGS build-up and formula-vs-formula comparison
Competitor Intelligence Agent
Ingredient decks, claims, price and formula gaps
Product Innovation Agent
Unmet-opportunity generation with rationale
Manufacturing Readiness Agent
Equipment fit, scale-up and process risk
Agent decision framework
Agents never return unrestricted answers. Every recommendation carries its evidence, confidence, assumptions, risks and the experiment that would validate it.
- Recommendation
- Evaluate Compound A as a tyrosinase inhibitor.
- Evidence
- Docking + 200 ns MD + published in-vitro enzyme data.
- Confidence
- Moderate.
- Assumptions
- Active reaches the basal layer at formulated concentration.
- Risks
- No strong human topical study identified.
- Validation required
- Enzyme inhibition assay, then melanocyte assay.
- Next experiment
- EXP-4419 — mushroom tyrosinase IC50, 3 replicates.