5 layers. 0 hallucinations. That's the distance between your AI agent and your core banking system.
In enterprise banking, an "AI Agent" is a Conversational Wrapper around a Deterministic State Machine. The LLM is the UI. The state machine decides. The policy engine enforces. Full audit trail. Zero hallucinated transfers.
"Send 50 euros to John"
Awaiting execution...
Every bank is deploying AI agents.
Nobody has answered the CRO's question.
"How do we deploy an LLM agent in a production banking workflow without it hallucinating a €50M transfer?" Your CTO is under pressure from the board. Your Head of Model Risk is under pressure from the ECB. The answer isn't a better prompt. It's a deterministic control plane.
fail in prod
surface area
guardrails
Five Layers. Zero Trust in the LLM.
The LLM is the last 10%. The other 90% is deterministic, auditable, and replayable.
Intent & Slot Parser
The TranslatorUnstructured text → strict JSON schema. Handles ambiguity, missing slots, invalid values. Rule-based + fine-tuned classifier + LLM for complex extraction. Output: a deterministic object.
Policy & Guardrails Engine
The BouncerAuth, authorization, limits, sanctions/AML, SCA. The "hard stop" before money moves. The LLM never sees this layer — it just gets a yes/no back.
State Machine Orchestrator
The ConductorDecides what happens next. INIT → VALIDATING → PENDING_SCA → EXECUTING → COMPLETED. The LLM doesn't decide the path. The state machine does. Deterministic. Replayable.
API Integration Layer
The HandsIdempotent. Structured I/O. No free text. The LLM never sees raw API errors. Retries, escalation, and fallbacks are deterministic — not generated.
Response Generator
The FaceThe ONLY layer where the LLM shines. Takes structured result, generates natural language. Templated for financial data. Never hallucinates numbers. Makes it sound nice.
Watch a €50 Transfer Execute
From natural language to bank core API. Five layers. Zero hallucination. Full audit trail. Click through.
Ready to Execute
"Send 50 euros to John" — click Next to watch the deterministic pipeline process this request.
Chatbot vs Production Agent
The difference isn't the model. It's everything around it.
| Dimension | Chatbot | ORAIflow Agent |
|---|---|---|
| Decision authority | The LLM decides everything | The state machine decides. The LLM only speaks. |
| Output | Free text. Unstructured. Unverifiable. | Strict JSON. Typed. Validated. Replayable. |
| Error handling | LLM "explains" the error to the user | Deterministic fallback. Escalate. Retry. Never guess. |
| Compliance | Prompt: "Please follow regulations" | Policy engine enforces. Non-negotiable. Hard stop. |
| Audit trail | "The AI said it did it" | Full state-graph. Every transition logged. Deterministic replay. |
| Hallucination surface | Entire conversation. Numbers, names, amounts. | Only the final sentence. Templated. Numbers never generated. |
| Scalability | More users = more LLM calls = more risk | LLM is 10% of the stack. 90% is deterministic and free. |
Built for Regulated Environments
Not a GPT wrapper. A deterministic control plane with an LLM as the UX layer.
Deterministic State Machine
Every workflow is a state graph. Reproducible. Replayable. Challengeable. Your auditor gets a timeline, not a shrug.
RAG-Aware Context
Retrieval-augmented generation for domain knowledge. The LLM answers from YOUR policy docs, not from training data. Grounded. Verifiable.
MCP Compatible
Model Context Protocol native. Connect any LLM, any tool, any data source. The control plane doesn't care which model sits on top.
Structured I/O
Inputs and outputs are strictly typed JSON. No free text in the pipeline. The LLM never sees raw API errors. Only structured results.
Idempotent Execution
Safe retries. No duplicate transfers. No double-charges. The API layer guarantees exactly-once semantics regardless of network failures.
Templated Output
The LLM generates the sentence, not the numbers. Amounts, IDs, dates — all injected from the state machine. Zero hallucinated figures.
The LLM is the Face.
The State Machine is the Brain.
The "magic" isn't in the LLM generating creative text. It's in the robustness of the parsing layer and the security of the execution layer. The LLM is merely the UI/UX engine that translates human language into structured data for the backend workflows.
If your AI agent must survive a model risk committee review, ORAIflow is your control plane.
# Deterministic agent — the LLM is just the UI from oraiflow import Agent, StateMachine, PolicyEngine agent = Agent( parser = 'intent_slot', # "Send €50 to John" → JSON policy = 'bank_compliance', # Auth, limits, AML, SCA state = 'sepa_transfer', # INIT→VALIDATE→SCA→EXEC api = 'core_banking', # Idempotent, typed I/O response= 'templated_llm', # LLM makes it sound nice ) # User says: "Send 50 euros to John" result = agent.execute("Send 50 euros to John") # System does (deterministic, logged, replayable): # → PARSE: {intent: SEPA, amount: 50, to: USER_12345} # → GUARD: auth ✓ · limits ✓ · AML ✓ · SCA pending # → ROUTE: INIT → VALIDATING → PENDING_SCA # → EXEC: bank_core.sepa_transfer(tx) → TXN_999 # → REPLY: "Your €50 transfer to John succeeded. ID: TXN_999" print(result.trace) # Full state-graph. Replayable. print(result.audit) # Every layer logged. Timestamped.
Your AI agent is going to production.
Make sure it can't hallucinate a transfer.
We'll model your workflow. You get the state machine, the policy rules, the parser config, and the audit trail template. Free. No obligation. No sales call.