Enterprise AI + Quantum FDE OS · 14 modules · 140 MCQs
Deep dive: AI, agents and quantum on the enterprise front line
Every module below carries the same four artefacts an FDE actually needs: the workflow, the architecture, a live simulator, and the use cases across data centre, business data and applications — plus ten multiple-choice questions to check understanding. All data is synthetic and every calculation is deterministic.
$ python -m fde_toolkit deepdive
Probabilistic AI proposes. Deterministic controls decide whether the proposal can become an action.
FDE operating model
AI insights · discovery · ROI framing
Turn a messy customer problem into a scoped, measurable, production-bound AI workflow.
$ python -m fde_toolkit deepdive --module briefing
Cohort orchestration
AI nudge · human operating layer
Route scarce human attention to the participants and teams that are actually stuck.
$ python -m fde_toolkit cohort
Environment diagnostics
Data-centre telemetry · infra health scoring
Establish whether the environment can actually run the system before anything else.
$ python -m fde_toolkit diagnose
Ontology and knowledge graph
Knowledge graph · GNN · semantic mapping
Map cryptic legacy schemas onto an enterprise semantic model the agent can reason over.
$ python -m fde_toolkit ontology
Memory fabric
Vector DB · episodic and semantic memory
Scratchpad, episodic and semantic tiers with eviction, provenance and validation.
$ python -m fde_toolkit memory
Tool synthesis (MCP)
Agentic AI · MCP · API governance
Turn an OpenAPI surface into scope-gated, risk-annotated tools the agent may call.
$ python -m fde_toolkit tools
Intent routing and SLM
SLM · classifier · inference control plane
Send the cheap, certain work to a small model and reserve the frontier path for risk.
$ python -m fde_toolkit router --query "..."
Agentic ReAct loop
Agentic AI · tool use · inferencing
Reason, act, observe, validate — with a policy gate before anything irreversible.
$ python -m fde_toolkit react
DAG orchestration
Workflow orchestration · parallel agents
Explicit dependencies, parallel lanes and a visible critical path.
$ python -m fde_toolkit dag --workflow aml
RAG and retrieval
RAG · vector DB · reranking
Hybrid retrieval with metadata filters, reranking, citation and permission checks.
$ python -m fde_toolkit rag --query "..."
Policy as code and Q-LOCK
OPA / Rego · deterministic control
Deny-overrides guardrails that sit between the model's proposal and the real action.
$ python -m fde_toolkit policy --scenario structuring
Audit and evidence
Immutable trail · regulatory replay
Every routed intent, tool call, policy decision and override, in order, replayable.
$ python -m fde_toolkit audit
SEOS AI and quantum
Neural ODE · QFT · QSVM · neural network OS
Continuous-time signal modelling plus hybrid quantum feature experiments, benchmarked.
$ python -m fde_toolkit seos
Neural feedback loop
Evaluation · drift · champion / challenger
Turn every override, false positive and incident into a governed model or policy change.
$ python -m fde_toolkit retro
Layer stack
L0 enterprise → L9 audit and feedback
| Layer | Components | Outputs |
|---|---|---|
| L0 · Data centre | Servers, GPUs, storage, network, Kubernetes, power and cooling | Telemetry, logs, traces, metrics |
| L1 · Business data | Customers, accounts, transactions, invoices, KYC, risk, exposure | Structured records and documents |
| L2 · Applications | APIs, CRM, core banking, ERP, payment rails, workflow systems | Events, API calls, business state |
| L3 · Data pipelines | CDC, batch and stream, schema registry, data quality, lineage | Canonical enterprise events |
| L4 · Knowledge fabric | Vector DB, knowledge graph, network graph, metadata and provenance | Retrieval context and relationships |
| L5 · AI / ML fabric | SLM and LLM, classifiers, anomaly detection, time series, GNN, ranking | Predictions, embeddings, scores |
| L6 · Agent runtime | Intent router, ReAct, DAG, MCP tools, tiered memory | Plans, tool calls, observations |
| L7 · Policy and quantum | OPA/Rego, deterministic Q-LOCK, hybrid QSVM/QAOA experiments | Allow / deny / escalate and optimisation |
| L8 · Human control | AI assist, AI nudge, review queues, approvals, explainability | Human decision or override |
| L9 · Audit and feedback | Immutable audit, evaluation, drift, telemetry, retro | Evidence plus model and policy improvement |
Synthetic enterprise telemetry
240 deterministic events, no real data
events
240
mean risk
0.297
mean latency
107 ms
mean RAG score
0.631
data quality
92.6%
infra health
57.7%
Question bank
10 per module
140 multiple-choice questions are wired into the quiz tab of each module page, with answer keys rotated so the correct option is not always the first.
Function outputs
what each module actually returns
| Module | Input | Signal | Score | Decision |
|---|---|---|---|---|
| OntologyMapper | TBL_CUST_99X_REV | Customer | 0.99 | AUTO_ACCEPT |
| OntologyMapper | EXP_LMT_TB | CreditExposure | 0.98 | AUTO_ACCEPT |
| ToolSynthesizer | POST /payments | create_payment | HIGH | REQUIRES_APPROVAL |
| IntentRouter | What is our exposure? | Compliance / graph agent | 0.94 | ROUTE |
| RAGRetriever | EDD refresh policy | sentence chunk #4 | 0.502 | RETRIEVE |
| PolicyEngine | TXN-10021 | ALLOW | 0 matched rules | ALLOW |
| PolicyEngine | TXN-20051 | $50M shell beneficiary | V1+V4 breach | REJECT_ISOLATE |
| ReActAgent | AML investigation | tool → observation → tool | 4 steps | COMPLETE |
| DAGOrchestrator | AML case | 6 tasks | critical path 4.2s | SCHEDULE |
| Memory | small-alert policy | episodic memory | 0.91 similarity | RECALL |
| SEOS / NeuralODE | telemetry stream | drift amplitude | 0.4469 | STABLE |
| SEOS / QFT | 28 telemetry vars | 4-qubit state | coherence 0.837 | SYNC |
| SEOS / QSVM | compliance topology | margin | 0.850 | CLASSIFY |
| Q-LOCK | $50M transfer | V1 velocity + V4 ethics | BREACH | REJECT_ISOLATE |
| AI-Nudge | KYC expired + payment request | nudge | 0.96 | ESCALATE |
| AI-Assist | VP Compliance summary | 3 findings + evidence | 0.93 | DRAFT |
| NeuralFeedback | false-positive alert | policy / model feedback | Δprecision +0.04 | LEARN |
Simulator logic
every formula, written out
Risk score
0.18·CPU + 0.15·latency + 0.12·error + 0.14·velocity + 0.10·concentration + 0.12·(1−provenance) + 0.10·(1−ethics) + 0.05·(1−jurisdiction) + 0.04·(1−liquidity)
→ 0..1 deterministic risk
Q-LOCK
reject if velocity > 0.85 OR ethics < 0.35 OR sanctions = 1 OR (amount ≥ 10 000 AND dual_approval = 0)
→ REJECT_ISOLATE
RAG hybrid
0.6·dense + 0.4·keyword, then metadata and permission filters
→ retrieval rank
AI nudge
if high-risk signal AND missing prerequisite → next best action
→ ESCALATE / REQUEST EVIDENCE
AI assist
summarise evidence and recommended action; human signs off
→ DRAFT / REVIEW
Quantum hybrid
encode normalised feature vector for QSVM/QAOA; compare against classical baseline
→ candidate classification
Feedback
capture outcome, label, drift and override; update eval set and challenger
→ champion / challenger evidence
Stack diagram
ASCII, terminal-ready
ENTERPRISE AI + QUANTUM FDE OS
+------------------------------------------------------------------+
| HUMAN / EXECUTIVE CONTROL |
| AI Assist | AI Nudge | Review Queue | Approvals | Explainability|
+-------------------------------+----------------------------------+
|
+-------------------------------v----------------------------------+
| GOVERNANCE + DETERMINISTIC CONTROL |
| Policy-as-Code | OPA/Rego | Q-LOCK | RBAC | ABAC | Audit | SIEM |
+-------------------------------+----------------------------------+
|
+-------------------------------v----------------------------------+
| AGENT INTELLIGENCE LAYER |
| Intent Router | SLM | LLM | ReAct | DAG | MCP | Multi-Agent |
| Planner | Executor | Critic | Validator | Escalation |
+-----------+-------------------------------+----------------------+
| |
+--------v---------+ +--------v---------+
| KNOWLEDGE FABRIC | | MEMORY FABRIC |
| RAG | | Scratchpad |
| Vector DB | | Episodic |
| Knowledge Graph | | Semantic |
| Network Graph | | Context history |
+--------+---------+ +--------+---------+
| |
+-----------v-------------------------------v----------------------+
| AI / ML FABRIC |
| Classification | Forecasting | Anomaly | GNN | NLP | Ranking |
| Time series | Neural ODE | SLM | LLM | Multimodal | Ensembles |
+-------------------------------+----------------------------------+
|
+-------------------------------v----------------------------------+
| HYBRID QUANTUM LAYER |
| Feature encoding | QFT spectral | QSVM | QAOA | Benchmarking |
+-------------------------------+----------------------------------+
|
+-------------------------------v----------------------------------+
| DATA FABRIC |
| CDC | Streaming | Batch | APIs | Events | ETL/ELT | Quality |
| Lineage | Schema registry | Feature store | Synthetic data |
+-------------------------------+----------------------------------+
|
+-------------------------------v----------------------------------+
| ENTERPRISE |
| Data Center | Business Data | Applications | Networks | Devices |
+------------------------------------------------------------------+