CAIN-42 MCPGateAGENTIC AI 75 · #3
Closed-loop experimental discovery
Google laboratory agents used actual experimental results to revise aptamer-discovery strategies across successive cycles.
Last reviewed 2026-11-11
Agentic AI 75 / #3
2026 status: Research demonstration · Category: Scientific discovery
The 2026 development
Google laboratory agents used actual experimental results to revise aptamer-discovery strategies across successive cycles.
The governance gap
A loop that learns from physical results will also learn from contaminated, delayed or mislabeled results. The evidence chain has to separate what was observed from what was inferred, and it has to survive a bad cycle.
Where CAIN-42 fits: Moat 7 — Autonomous Trust Control Loop
Core question: Can verified experience change future trust? Monitor, detect, contain, recover, and re-authorise from observed outcomes.
The control loop needs a fact-segregated record: observed, verified, derived, unverified and counterfactual kept apart. A cycle may change strategy, but it may not silently promote an unverified inference into a trusted premise.
How the wiring works
- Record each cycle's inputs and observed outputs with an explicit fact class.
- Require the strategy change to cite the observations that caused it.
- Re-score the new strategy against retained evidence before the next physical step.
- Halt the loop when evidence quality falls below the declared floor.
What is real today
LIBRARY Production code and tests exist in this repository; there is no hosted route for this exact use.
The fact-class and cycle-control primitives exist and are tested. No hosted closed-loop scientific workflow is running here, and no external lab result has been validated.
| Component in this repository | What the module says it is |
|---|---|
cain/argus/evolution.py | CAIN ARGUS — self-evolution governance (Prompt 14, section 13) and world-model assurance (section 14). |
cain/continuum/assurance.py | CAIN CONTINUUM — Continuous Agent Assurance (Prompt 11, Phase 29). |
cain/runtime/world_model.py | CAIN Sovereign — Self-Evolving World Model adaptation layer (Prompt 7, Phase 12). |
cain/causality/graph.py | CAIN CAUSALITY — Causal Event Graph (Prompt CAUSALITY, Phase 3). |
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 | Discovery Loop Evidence Review |
|---|---|
| Buyer | AI-for-science teams and research foundations |
| Price (proposal) | $5,000–$12,000 per review |
| 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-A75-003 — Discovery Loop 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): $5,000–$12,000 per review · 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.