# Implementing Policy Engine and Decision Rules in Semantica: Architecture and Code Examples

> Implement Semantica's policy engine for rule lifecycle management, automated compliance, and versioned decisions. Explore architecture and code examples for effective policy implementation.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: architecture
- Published: 2026-08-18

---

**Semantica's policy engine provides a full-stack solution for rule lifecycle management, automated compliance checking, and versioned decision tracking through the `PolicyEngine` class located in [`semantica/context/policy_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/policy_engine.py).**

The semantica-agi/semantica repository delivers a graph-native framework that enables AI agents to enforce business rules without code changes. By abstracting the storage layer through the `GraphStore` interface, the system supports Neo4j, FalkorDB, and in-memory backends while maintaining complete audit trails for every policy decision and exception.

## Core Architecture and Components

The policy engine orchestrates CRUD operations, versioning, compliance validation, and impact analysis within a unified architecture. Understanding these components is essential for implementing rule-driven decision making in your Semantica applications.

### The PolicyEngine Class

The central orchestrator resides in [`semantica/context/policy_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/policy_engine.py). When instantiated, `PolicyEngine` accepts a generic `graph_store` backend and automatically detects Cypher support via `self._supports_cypher`. This detection allows the engine to switch between optimized Cypher queries and generic node/edge APIs, ensuring compatibility across different graph databases.

### Data Models and Types

Policy and decision structures are defined as dataclasses in [`semantica/context/decision_models.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/decision_models.py). The **Policy** model (lines 74–87) captures rule sets, versioning metadata, and categorical tags, while the **Decision** model (lines 86–98) records outcomes, confidence scores, and arbitrary metadata fields. These models enforce type safety throughout the compliance pipeline.

### Graph Store Abstraction

The engine relies on the `GraphStore` interface imported from [`semantica/graph_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/graph_store.py). This abstraction provides methods like `add_node`, `add_edge`, `find_nodes`, and `execute_query`, allowing the policy engine to function independently of the underlying database technology.

## Policy Lifecycle Management

Semantica treats policies as versioned entities, enabling immutable rule histories and safe iteration workflows.

### Creating and Storing Policies

The `add_policy` method (lines 98–108) persists a `Policy` dataclass to the graph store. It validates unique `policy_id` values and timestamps the operation using `datetime.utcnow()`. When using a Cypher-enabled backend, the method executes a `CREATE` statement; otherwise, it falls back to the generic `add_node` API.

```python
from semantica.context import PolicyEngine, Policy
from datetime import datetime

engine = PolicyEngine(graph_store=my_graph_store)

policy = Policy(
    policy_id="loan_policy_001",
    name="Standard Loan Approval",
    rules={"min_credit_score": 650, "max_loan_amount": 500_000},
    category="lending",
    version="1.0",
    created_at=datetime.utcnow(),
    updated_at=datetime.utcnow(),
    metadata={"entities": ["customer"]},
)
engine.add_policy(policy)

```

### Version Control and Updates

Updating rules via `update_policy` (lines 166–190) automatically triggers the `_generate_next_version` logic. Rather than mutating existing nodes, the engine creates a new `Policy` instance with an incremented version string and links it to the previous version using a `VERSION_OF` edge. This immutable approach preserves historical decision contexts while enabling rollback capabilities.

To retrieve the active rule set, `get_applicable_policies` queries for the latest version by excluding any node that has a downstream `VERSION_OF` edge, ensuring only current policies affect new decisions.

## Implementing Decision Rules and Compliance

The compliance engine evaluates decisions against policy rule sets using a flexible prefix-based syntax.

### Rule Syntax and Operators

Rules are defined as dictionaries where keys use specific prefixes to indicate validation logic:

- **`min_<field>`**: Validates that decision metadata meets or exceeds a numeric threshold
- **`max_<field>`**: Ensures metadata values do not exceed specified limits
- **`required_<field>`**: Confirms metadata contains all required items or matches a scalar value
- **`min_confidence`**: Enforces a minimum confidence score on the decision itself
- **`allowed_outcomes`**: Restricts decisions to specific outcome strings
- **`required_categories`**: Validates that decision categories match policy scopes

### Compliance Evaluation Flow

The `check_compliance(decision, policy_id)` method (lines 78–88) fetches the target policy and delegates evaluation to `_evaluate_compliance` (lines 996–1025). This internal method iterates through the policy's rule dictionary, applying the prefix logic to the decision's metadata and intrinsic fields.

```python
from semantica.context import Decision

decision = Decision(
    decision_id="dec_123",
    category="loan_approval",
    outcome="approved",
    confidence=0.96,
    metadata={"credit_score": 720, "loan_amount": 350_000},
)

is_compliant = engine.check_compliance(decision, policy_id="loan_policy_001")

```

The `_check_compliance_with_rules` helper supports complex boolean logic, allowing policies to combine multiple constraints into a single validation pass.

## Impact Analysis for Rule Changes

Before deploying policy updates, administrators can simulate effects using the impact analyzer. The `analyze_policy_impact` method (lines 712–777) executes a "what-if" query to identify all decisions linked to the target policy, then invokes `_calculate_impact_metrics` to compute:

- The total number of affected historical decisions
- Projected changes in compliance scores
- Estimated risk increases based on rule differentials
- Detailed diffs between current and proposed rule sets

```python
new_rules = {"min_credit_score": 750, "max_loan_amount": 550_000}
impact = engine.analyze_policy_impact(
    policy_id="loan_policy_001", 
    proposed_rules=new_rules
)

```

This simulation enables data-driven policy adjustments without risking production decision quality.

## Exception Handling and Audit Trails

When business constraints require bypassing standard policies, the `record_exception` method (lines 446–504) creates an immutable audit trail. The implementation generates an `Exception` node and creates two relationships: `GRANTED_EXCEPTION` linking to the decision, and `OVERRIDDEN_POLICY` linking to the waived policy.

```python
exception_id = engine.record_exception(
    decision_id="dec_123",
    policy_id="loan_policy_001",
    reason="VIP customer waiver",
    approver="senior_manager",
    justification="Strategic partnership",
)

```

This graph structure enables complete lineage queries, showing which decisions received exceptions, who authorized them, and which policies were overridden.

## Integration with Agent Workflows

The policy engine integrates seamlessly with Semantica's agent architecture through lazy initialization. The `AgentContext` class creates a `PolicyEngine` instance on first access via `self._policy_engine = PolicyEngine(knowledge_graph)`, making enforcement available to every agent without upfront configuration overhead.

Higher-level APIs in [`semantica/context/decision_methods.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/decision_methods.py) demonstrate production usage, automatically invoking `check_compliance` before persisting decisions and routing non-compliant outcomes for human review or exception recording.

## Summary

- **PolicyEngine** in [`semantica/context/policy_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/policy_engine.py) orchestrates all policy operations with backend-agnostic graph store support.
- **Immutable versioning** creates new policy nodes linked by `VERSION_OF` edges rather than mutating existing rules.
- **Prefix-based rules** (`min_`, `max_`, `required_`) enable declarative compliance logic without code deployment.
- **Impact analysis** simulates rule changes against historical decisions to predict compliance deltas before production deployment.
- **Exception tracking** maintains audit trails through dedicated nodes and relationships for regulatory compliance.

## Frequently Asked Questions

### How does Semantica handle policy versioning?

Semantica implements immutable versioning where `update_policy` creates a new node with an incremented version string and links it to the previous version via a `VERSION_OF` edge. The `get_applicable_policies` method automatically filters for the latest version by excluding nodes with outgoing `VERSION_OF` relationships, ensuring agents always evaluate against current rules while preserving complete historical context.

### What rule types are supported by the compliance engine?

The engine supports metadata constraints through prefixes including `min_<field>`, `max_<field>`, and `required_<field>`, alongside decision-specific checks like `min_confidence`, `allowed_outcomes`, and `required_categories`. These rules are evaluated against both the decision's metadata dictionary and its intrinsic fields (confidence, outcome) within the `_evaluate_compliance` method.

### Can the policy engine function without Neo4j?

Yes. The `PolicyEngine` class operates through the abstract `GraphStore` interface, automatically detecting Cypher support via `self._supports_cypher`. When Cypher is unavailable, the engine falls back to generic methods like `add_node`, `find_nodes`, and edge traversals, enabling operation with FalkorDB, in-memory stores, or any custom implementation of the graph store contract.

### How are policy exceptions audited?

Exceptions are recorded as dedicated nodes with unique IDs via `record_exception`. The system creates `GRANTED_EXCEPTION` edges linking decisions to their exceptions and `OVERRIDDEN_POLICY` edges linking exceptions to the waived policies. This graph structure supports complete lineage queries, allowing auditors to trace which decisions bypassed specific rules, who authorized the waivers, and the business justifications provided.