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Sample_ai_rules

TL;DR - Integrated Deterministic and AI Rules

Prompt 1 (Create System):

Create a system named basic_demo from samples/dbs/basic_demo.sqlite

Prompt 2 (Add NL Logic):

On Placing Orders, Check Credit:

1. The Customer's balance is less than the credit limit
2. The Customer's balance is the sum of the Order amount_total where date_shipped is null
3. The Order's amount_total is the sum of the Item amount
4. The Item amount is the quantity * unit_price
5. The Product count suppliers is the sum of the Product Suppliers
6. Use AI to Set Item field unit_price by finding the optimal Product Supplier based on cost, lead time, and world conditions

Use case: App Integration

1. Send the Order to Kafka topic 'order_shipping' if the date_shipped is not None.

(Developers review this DSL before execution, providing a natural human-in-the-loop checkpoint.)

Test in the Browser, verify the AI Audit

 

A Unified Model for Governable Creativity

AI also provides creativity and reasoning that businesses want... how do we provide that, with goverance?.

For example - a business can continue to operate even if a tanker has blocked the Suez canal by choosing a supplier:

Step 1. Create a new project (e.g., from the Manager)
genai-logic create --project_name=basic_demo_ai_rules --db_url=sqlite:///samples/dbs/basic_demo.sqlite
Step 2. Open the project; provide Copilot prompt for deterministic and probabilistic logic (rule 6)
Bootstrap Coplilot, and
Paste the logic above into your Copilot chat


Unified Deterministic and Probabilistic Logic


Enterprises want the best of both: the creativity of probabalistic logic, with the governability of deterministic logic -- all in one unified Business Logic Agent. Here's an example, and we then generalize.


A. Example: Choose Supplier, based on current world conditions

Agentic systems are evolving quickly, and a clearer architectural picture is forming:

Not AI vs Rules — AI and Rules together.

Different kinds of logic naturally call for different tools, as in this unified example:

  • Deterministic Logic — logic that must always be correct, consistent, and governed.
    Example: “Customer balance must not exceed credit limit.”

  • AI Logic — logic that benefits from exploration, adaptation, and probabilistic reasoning.
    Example: “Which supplier can still deliver if shipping lanes are disrupted?”

    • Creative reasoning needs boundaries.
      Deterministic rules supply the guardrails that keep outcomes correct, consistent, and governed.

And then, test via MCP-discovered API: Constraint blocks bad data: ️

Test Logic with MCP Discovery
On Alice's first order, include 100 Egyptian Cotton Sheets

Data Model, including AI Audit Trail


basic_demo_data_model


B. The Business Logic Agent


The Business Logic Agent processes a declarative NL requests:

  • At declaration time (e.g., in Copilot):

    • D1: Accepts a unified declarative NL request
    • D2. Uses GenAI to create
      • Rules (in Python DSL: Domain Specific Logic) for deterministic Logic
      • LLM calls for Probablistic

  • At runtime

    • R1: DSL is executed by the Rules Engine (deterministic - no NL pocessing occurs)
    • R2: LLM calls

Bus-Logic-Engine

Agentic systems become far more compelling when probabilistic intent is paired with deterministic enforcement.

This "governable intent" model aligns with enterprise expectations —
adaptive where helpful, reliable where essential.

The Business Logic Agent unifies probabilistic intent with deterministic enforcement in a single model


C. Echoes Modern Thinking


Lamanna: "Sometimes customers don't want the model to freestyle… They want hard-coded business rules." → Exactly this hybrid: probabilistic intent + deterministic enforcement

Governable AI


Heads-Up: AI-Enabled Projects

Copilot can help you understand, learn, and do... here's how


GenAI-Logic projects are already AI-enabled, meaning they come with built-in training materials (context engineering) that help assistants like GitHub Copilot, Claude, or ChatGPT understand your project context. For more information, see AI-Enabled Projects Overview.

Once you’ve completed this demo, try engaging your AI assistant directly — it already knows about your project’s structure, rules, and examples.

Understand GenAI-Logic by asking Copilot questions such as:

  • “Where are the declarative business rules defined?”
  • “Explain how credit-limit validation works in this project.”
  • “Show me how to add a new rule for discount calculation.”
  • “Walk me through the AI Guided Tour.”

Learn about GenAI-Logic with the AI-Guided Tour. Just ask Copilot: guide me through.

  • note: you should first delete logic/logic_discovery/place_order/check_credit.py)

In addition to all the things CoPilot can do natively, we've taught it about GenAI-Logic. Just ask Copilot: what can you help me with?