CAIN-42 MCPGateAGENTIC AI 75 · #12
Experimentally validated biological binder discovery
Google reported human–agent research producing high-affinity binders for a cancer-associated target.
Last reviewed 2026-11-11
Agentic AI 75 / #12
2026 status: Research demonstration · Category: Biotechnology
The 2026 development
Google reported human–agent research producing high-affinity binders for a cancer-associated target.
The governance gap
A computational result that later becomes a wet-lab decision needs a chain from model output to assay to conclusion. The failure mode is a confident generated candidate treated as a validated binder.
Where CAIN-42 fits: Moat 4 — Execution Provenance & Attestation
Core question: Can we prove what actually happened? Hash-chained, signed records binding decision to execution to observable effect.
Keep computational predictions and experimental observations in separate fact classes, and bind each conclusion to the assay that supports it. A prediction may guide the next experiment; it may not be reported as a result.
How the wiring works
- Record the model, version, inputs and generated candidates as predictions.
- Link each candidate to the assay protocol and the observed measurement.
- Require an independent review before any candidate is described as validated.
- Preserve negative and inconclusive results alongside positive ones.
What is real today
PROPOSED A design mapping only. No dedicated implementation exists yet; do not buy this as a finished control.
No biological workflow, assay data or wet-lab integration exists in this repository. This page is a governance design, not a scientific claim.
| Component in this repository | What the module says it is |
|---|---|
cain/argus/physical.py | CAIN ARGUS — physical-AI assurance (Prompt 14, section 15). SIMULATED. |
cain/runtime/research_agent.py | CAIN Sovereign — Autonomous Research Agent (Prompt 7, Phase 14). |
cain/worm_export.py | CAIN Trust Fabric - tamper-evident WORM Causal Evidence Fabric (Phase 4) |
cain/causality/evidence.py | CAIN CAUSALITY — Causal Evidence Independence Graph (Prompt CAUSALITY, Phase 9). |
Descriptions are the first line of each module's own docstring, read without importing it. A module path is a pointer into this repository, not a hosted endpoint. Its existence is checked by the test suite; its status for this use is the label above.
Check it yourself
- Inspect the live ARGUS status
/fabric/argus/status - Fire a live decision through the CAIN-42 pipeline
/fabric/try— the same enforcement path every product sits behind.
Commercial package
| Offer | Research Evidence Review |
|---|---|
| Buyer | Biotech platforms and research institutes |
| Price (proposal) | Advisory engagement from $10,000 |
| Bundle | Physical & Lab — A validity check between the plan and the machine. |
| Operator stream | Stream 2 of the 10-stream operator playbook |
Lab automation, robotics, industrial operations, construction technology is the bundle buyer. The entry offer for this bundle is Physical-action assurance review (advisory, simulation-first); the range is $5,000–$15,000 per engagement. A price on this page is a proposal, not a validated rate, and no customer is claimed.
Buy this as a product or service
CAIN-A75-012 — Research Evidence Review DELIVERABLE
A bounded engine run on the buyer's sample: findings, a written report, and a SHA-256 artifact manifest. The manifest is unsigned: it is not a signature and not an attestation.
Kind: Assessment · Price (proposal): Advisory engagement from $10,000 · Lead time: 5 business days.
Open the product page · all 75 products
What this page does not claim
What this page is not. CAIN-42 is not affiliated with, endorsed by, integrated into, or shipped with the organisation named in the 2026 item. The item is public context; the page describes how CAIN-42 would govern that class of capability. No page here claims a customer, a partnership, a certification, a patent, or a production integration. Prices are proposals. Built by a three-person team.