DEEP-DOMAIN SCIENTIFIC AGENT ARCHITECTURE · #0 FOUNDATION
Digitize the domain environment & expert methodology.
Zero-hallucination science from massive data to instant insight.
In deep verticals like semiconductor fabs, precision medicine, and aerospace, general LLMs are domain outsiders—tacit know-how is not on the public web, and every problem is tightly coupled to field roles, tool physics, proprietary data, and SOPs.
Built for High-Barrier Verticals · Semiconductor Fab · Clinical & Precision Medicine · Aerospace Manufacturing
Three-Layer Digitized Foundation · Governed Two-Speed Loop · Six Core Capabilities
Digitizes role responsibilities & approval gates, machine measurement physics, spatial coordinates, and target vs. reference baselines.
Encodes tacit engineering SOPs (e.g., wafer edge exclusion, chamber checks, denominator rules) into governed, non-skippable execution rails.
Models propose a Canonical Plan; 100% of numerical statistics & physical quantities are computed by multi-pass deterministic operators.
General LLMs lack fab/clinical SOPs and guess blindly.
Scenario + Expertise Package + Rails Kernel enforce expert workflows.
Raw wafer maps, point clouds & waveforms overflow context windows.
Control/Data separation: Scoped View + Query Catalog over engine slicing.
Probabilistic text generation cannot guarantee physical or statistical rigor.
Canonical Plan + 21-Pass deterministic operators; zero LLM math guessing.
Free-text answers cannot prove numbers match actual executions.
Cryptographic receipts, denominator definitions & Slot Verification.
Ad-hoc charts suffer from binning distortion or chart-number mismatch.
Source-locked charts & underlying arrays with drill-down & state restore.
Heavy batch compute needs approval; follow-up Q&A needs sub-second speed.
Analysis Agent (immutable assets) + Inquiry Agent (instant QueryPlan).
Observations (People · Machines · Data)
Deterministic 21-Pass Pipeline
Auditable Evidence & Instant Inquiry
01Architectural foundation (#0 premise)
MANGO AI / ARCHITECTUREIn common-sense office tasks, pretrained models already know the workflow. In semiconductor fabs, clinical medicine, and aerospace, the base model is a domain outsider.
Critical troubleshooting know-how—how PE, PIE, and YE engineers coordinate, why wafer edge exclusion is mandatory before defect-density math, or which chamber parameter to inspect when a ring or center-cluster pattern appears—never lives on the public web. Every problem is tightly coupled to a proprietary field environment of People (roles & approvals), Machines (measurement physics & tool limits), Data (spatial coordinates & target/reference baselines), and Methodology (troubleshooting SOPs & statistical denominators).
Instead of trying to smear domain knowledge into black-box model weights via fine-tuning, our architecture explicitly digitizes the field environment and expert methodology across three layers: ScenarioSpec (field context), Expertise Package (governed rails, methods & checks), and Scientific Pipeline (MethodDefinition M01–M15 deterministic operators).
L1 · ScenarioSpec: Digitizes field roles, tool physics, spatial coordinates & reference baselines.
L2 · Expertise Package: Locks senior engineers’ troubleshooting SOPs & checks into non-skippable rails.
L3 · Scientific Pipeline: Executes validated domain operators (M01–M15) with zero numerical hallucination.
026 Core Problem Spaces
PROBLEM SPACES & SOLUTIONSOur agentic architecture is purpose-built for deep-domain environments that simultaneously demand all six of the following capabilities—where general-purpose agents break down.
Pain point: Heavy reliance on internal fab/clinical context and years of tacit troubleshooting know-how; general LLMs guess blindly and skip critical checks. Architectural solution: Scenario + Expertise Package + Rails Kernel turn field context and expert SOPs into governed, non-skippable execution rails.
Pain point: High-res wafer maps, point clouds, full-wafer coordinates, and long waveforms never fit in a context window. Architectural solution: The model only inspects a controlled Scoped View and structured Query Catalog, while the underlying engine slices and computes raw data.
Pain point: Every statistic, distribution, and physical quantity impacts yield, safety, or clinical diagnosis—zero tolerance for LLM math hallucinations. Architectural solution: Models only interpret goals and propose a Canonical Plan; all numerical values are produced by 21-pass deterministic operators.
Pain point: Isolated conclusions and free-text generation cannot guarantee that "the number stated is the number computed" or explain exact denominators. Architectural solution: Binds Scientific Method physical definitions, logs every execution receipt, and enforces strict Slot Verification on final answers.
Pain point: Engineers must visually inspect high-precision distributions, heatmaps, and histograms; ad-hoc charts suffer from binning distortion or state loss. Architectural solution: Charts and underlying distribution arrays are co-generated and bi-directionally locked, supporting click-to-expand, source verification, and exact state recovery.
Pain point: Deep scientific pipelines are compute-heavy and require human approval, while follow-up engineering inquiry demands sub-second interactivity. Architectural solution: Dual-engine decoupling—an Analysis Agent (governed pipeline execution producing immutable assets) + an Inquiry Agent (sub-second multi-turn insights via QueryPlan).
Summary: General agents make simple tool calls in common-sense settings. Our architecture encapsulates a high-barrier industry’s complete field environment and expert methodology—automating scientific data production and engineering insight discovery end to end.
03Evidence & scientific argument
A useful scientific explanation connects the question, the method, the calculation and the limits of the conclusion. Explore a small worked example below.
Calculated from the complete synthetic observations. Open the value to inspect its formula, source data and scientific limits.
Does the observed difference support a persistent change?
Open a value to inspect its calculation and source observations. A numerical check does not establish the scientific interpretation.
01 / Frame
Two synthetic groups have different observed means. Does this support a persistent change?
Explore at your own pace.
An illustrative example, not a live autonomous agent or a performance benchmark. Values are calculated from synthetic observations; plans and assessments are prewritten. Follow-up measurements and revised analyses have not been performed.
04Research exchange
OPEN QUESTIONSWe welcome conversations with scientists, engineers and researchers building AI for scientific work.
Read & join the discussionArticles and topics have permanent pages to read and share.
How can domain expertise guide method and parameter choices?
What evidence should remain connected across a long investigation?
How should we evaluate whether a finding is ready to use?
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