LabOS is not a "natural-language protocol submission engine." That's a chatbot wrapper on SiLA2 — table stakes by 2026. LabOS is an agent mesh whose product is the experimental graph: every datapoint cryptographically tied to the typed chain of operations that produced it. This is what regulators want, what pharma BD pays for, and what trains the next generation of self-improving protocols.
| Agent | Owns | Inputs | Outputs | Backed by |
|---|---|---|---|---|
| Hypothesis | Target / mechanism proposals | OmicsOS embeddings, literature, Apollo cohort | Ranked hypotheses with uncertainty | Frontier LLM + RAG over Atlas |
| Design | Hypothesis → executable experiment graph (DAG) | Reagent catalog, instrument capability registry, budget | Typed DAG of ops, power calcs, success criteria, cost | Protocol IR + planner LLM |
| Execution | Brokers + monitors partner-lab runs | DAG, CRO/cloud-lab API state, SLA | Real-time status, anomaly flags, dynamic re-routing | State machine + LLM exception handler |
| QC / Validator | Decides whether output is trustworthy | Raw data, instrument metadata, spike-ins, controls | Pass/fail + uncertainty quantification | Statistical models + vision/sequence models |
| Learning | Updates priors, refines protocols, posts to Atlas | All graph nodes + outcomes + scientist feedback | Updated model weights, protocol patches, Atlas entries | RLHF + offline distillation pipeline |
A typed intermediate representation for lab operations — typed extension of PAML/Aquarium. Stable contract between Design and Execution. Without this, every CRO integration is bespoke and doesn't compound.
"npm for lab ops." Every CRO and instrument publishes a capability manifest (operations supported, specs, turnaround, cost). Execution Broker reads this to make smart routing decisions instead of being a Jira board.
Every datapoint cryptographically tied to its DAG path (Ed25519 + content-addressed graph store). Unlocks regulatory submission, IP defense, and pharma audit in one mechanism.
Held-out historical programs where agents must recover known answers. Without this, you can't claim the system works; with it, you've defined the category and have a reproducible benchmark for investors and regulators.
Year-1 LabOS runs on frontier LLMs (Opus 4.7, GPT-5, Gemini 2.5). Every margin point is Anthropic's or OpenAI's, every latency hit is theirs, and Apollo patient data cannot legally leave India under DPDP Act 2023. Fine-tuning is therefore both a margin lever and a regulatory necessity — not a "nice to have."
Opus 4.7 / GPT-5 / Gemini 2.5 behind a model-router. Capture every prompt/response pair as supervised distillation data. No regulated patient data sent to frontier APIs — proxied through on-prem retrieval.
Fine-tune Llama 4 / Qwen3 / Mistral-Bio on India cohort + Atlas + captured trajectories. Deploy on AWS Hyderabad (MeitY-compliant) and Apollo on-prem nodes. Target: 80% of agent calls served by sovereign model, 20% fallback to frontier.
Oncology, rare disease, cardiometabolic — separately fine-tuned heads sharing a base. Each vertical's model is a licensable asset. Sovereign deployment becomes a sellable feature to MNC pharma (data residency compliance bundled).
Every closed-loop cycle becomes training data. RLHF from scientist sign-offs. The model gets better the more LabOS is used — the compounding loop OpenAI cannot replicate without our data substrate.