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Series: S5 Intelligent Decisions · Article 1 | Level: Advanced | Reading Time: 20 min

From Data to Decision: The Four-Step Enterprise Intelligence Loop

#TL;DR

Enterprise intelligent decision-making is not a one-shot process but a closed-loop system composed of Sense → Think → Decide → Act. coomia-dip's Reasoning & Decision Layer (Reasoning & Decision Engine) and Agent Runtime Layer (Agent Runtime) work together to implement this loop, transforming raw data into actionable intelligent decisions. This article dissects the architecture design, data flow mechanisms, and inter-engine collaboration patterns of these four stages, helping you understand how coomia-dip builds enterprise-grade intelligent decision-making capabilities.

#1. Why a Four-Step Closed Loop

#1.1 Pain Points of Traditional Decision Making

In traditional enterprise systems, the decision process is often fragmented:

Code

Traditional Model:
┌──────────┐   Manual    ┌──────────┐   Manual    ┌──────────┐
│  Data    │ ──────────→ │ Analysis │ ──────────→ │  Human   │
│ Warehouse│             │  Report  │             │ Decision │
└──────────┘             └──────────┘             └──────────┘
     │                        │                        │
     │  Latency: hours/days   │  Latency: days/weeks   │  Latency: days/months
     ▼                        ▼                        ▼
  Data Silos           Fragmented Analysis      Non-traceable Decisions

This model has three major problems:

Problem	Manifestation	Impact
Decision Delay	Days to weeks from data to action	Missed optimal timing
Context Loss	Each step is isolated, information breaks	Degraded decision quality
Non-traceability	Cannot trace "why was this decision made"	High compliance risk

#1.2 Core Philosophy of Closed-Loop Decision Making

coomia-dip draws from the military OODA loop (Observe-Orient-Decide-Act) and upgrades it to an enterprise-grade STDA loop:

Code

                    ┌─────────────────────────────────────┐
                    │      STDA Intelligent Decision Loop  │
                    │                                     │
                    │   ┌──────┐    ┌──────┐              │
                    │   │Sense │───→│Think │              │
                    │   │      │    │      │              │
                    │   └──────┘    └──┬───┘              │
                    │       ▲          │                   │
                    │       │          ▼                   │
                    │   ┌───┴──┐    ┌──────┐              │
                    │   │ Act  │←───│Decide│              │
                    │   │      │    │      │              │
                    │   └──────┘    └──────┘              │
                    │                                     │
                    └─────────────────────────────────────┘

Each step has corresponding engines and data structures, forming a complete traceable chain.

#2. Sense Stage: From Raw Data to Semantic Events

#2.1 Data Source Ingestion

The Sense stage unifies scattered data sources into Ontology semantic events. coomia-dip supports multiple data ingestion methods:

Python

# Data source definition in coomia-dip
from ontology_sdk.models import DataSource, EventStream

class SenseLayer:
    """Sense Layer: Transforms raw data into semantic events"""

    def __init__(self, ontology_client, event_bus):
        self.ontology = ontology_client
        self.event_bus = event_bus
        self._sources: dict[str, DataSource] = {}

    async def register_source(
        self,
        source_id: str,
        source_type: str,      # "database" | "api" | "stream" | "file"
        connection_config: dict,
        mapping_rules: list[dict],
    ) -> DataSource:
        """Register a data source and configure mapping rules"""
        source = DataSource(
            source_id=source_id,
            source_type=source_type,
            config=connection_config,
            mappings=mapping_rules,
        )
        self._sources[source_id] = source
        return source

    async def ingest_event(
        self, source_id: str, raw_data: dict
    ) -> list["OntologyEvent"]:
        """Transform raw data into Ontology events"""
        source = self._sources[source_id]
        events = []
        for mapping in source.mappings:
            event = self._apply_mapping(raw_data, mapping)
            if event:
                events.append(event)
                await self.event_bus.publish(event)
        return events

    def _apply_mapping(self, raw: dict, mapping: dict) -> "OntologyEvent | None":
        """Apply mapping rules to transform raw data fields to Ontology properties"""
        object_type = mapping["target_object_type"]
        field_map = mapping["field_mappings"]

        properties = {}
        for raw_field, onto_prop in field_map.items():
            if raw_field in raw:
                properties[onto_prop] = raw[raw_field]

        if not properties:
            return None

        return OntologyEvent(
            event_type="property_change",
            object_type=object_type,
            properties=properties,
            source_id=raw.get("id"),
            timestamp=datetime.utcnow(),
        )

#2.2 Semantic Event Processing

Raw data passes through the following pipeline to become semantic events:

Code

Raw Data Processing Pipeline:

┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐
│  Raw     │   │  Schema  │   │  Type    │   │  Enrich  │   │ Ontology │
│  Ingest  │──→│  Validate│──→│  Convert │──→│  Context │──→│  Event   │
│          │   │          │   │          │   │          │   │          │
│ JSON/CSV │   │ Field    │   │ Type     │   │ Context  │   │ Semantic │
│ Protobuf │   │ Validate │   │ Normalize│   │ Enrich   │   │ Publish  │
└──────────┘   └──────────┘   └──────────┘   └──────────┘   └──────────┘

#2.3 The Role of DerivedPropertyService

In the Sense stage, DerivedPropertyService plays a critical role -- it computes derived properties from base properties:

Python

class DerivedPropertyService:
    """Derived Property Service: Computes higher-order semantics from base properties"""

    def __init__(self, property_registry, dependency_graph):
        self.registry = property_registry
        self.dag = dependency_graph

    async def compute_derived(
        self, object_type: str, object_id: str, changed_properties: set[str]
    ) -> dict[str, Any]:
        """When base properties change, compute affected derived properties
        following the dependency graph"""
        affected = self.dag.get_downstream(object_type, changed_properties)
        results = {}

        # Topological sort to ensure dependency order
        for prop_def in self.dag.topological_sort(affected):
            inputs = await self._gather_inputs(
                object_type, object_id, prop_def.dependencies
            )
            value = await prop_def.compute_fn(inputs)
            results[prop_def.name] = value

        return results

    async def _gather_inputs(
        self, object_type: str, object_id: str, deps: list[str]
    ) -> dict[str, Any]:
        """Gather input property values needed for computation"""
        obj = await self.registry.get_object(object_type, object_id)
        return {dep: getattr(obj, dep, None) for dep in deps}

#3. Think Stage: How the Reasoning Engine Works

#3.1 ReasoningEngine Architecture

The Think stage is the "brain" of the entire loop. coomia-dip's ReasoningEngine adopts a hybrid reasoning architecture:

Code

ReasoningEngine Internal Architecture:

┌─────────────────────────────────────────────────────────────┐
│                     ReasoningEngine                          │
│                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐ │
│  │ Rule-Based  │  │ ML-Based    │  │ Hybrid Reasoning    │ │
│  │ Reasoning   │  │ Inference   │  │ Orchestrator        │ │
│  │             │  │             │  │                     │ │
│  │ ┌─────────┐ │  │ ┌─────────┐ │  │ ┌─────────────────┐ │ │
│  │ │ Forward │ │  │ │ Model   │ │  │ │ Strategy Router │ │ │
│  │ │ Chain   │ │  │ │ Registry│ │  │ │                 │ │ │
│  │ └─────────┘ │  │ └─────────┘ │  │ │ rule_first      │ │ │
│  │ ┌─────────┐ │  │ ┌─────────┐ │  │ │ ml_first        │ │ │
│  │ │ Rete    │ │  │ │ Feature │ │  │ │ ensemble        │ │ │
│  │ │ Network │ │  │ │ Extract │ │  │ │ cascade         │ │ │
│  │ └─────────┘ │  │ └─────────┘ │  │ └─────────────────┘ │ │
│  └─────────────┘  └─────────────┘  └─────────────────────┘ │
│                                                             │
│  ┌──────────────────────────────────────────────────────┐   │
│  │                 Explanation Builder                    │   │
│  │  Records reasoning at each step for explainable AI    │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

#3.2 Forward-Chain Reasoning Flow

The core reasoning mode of ReasoningEngine is Forward Chaining:

Python

from dataclasses import dataclass, field
from typing import Any, Callable

@dataclass
class Fact:
    """A fact in the reasoning system"""
    name: str
    value: Any
    source: str           # Fact origin (sensor/derived/inferred)
    confidence: float = 1.0
    timestamp: float = 0.0

@dataclass
class Rule:
    """A reasoning rule"""
    rule_id: str
    name: str
    priority: int = 0
    conditions: list[Callable] = field(default_factory=list)
    actions: list[Callable] = field(default_factory=list)

    def evaluate(self, facts: dict[str, Fact]) -> bool:
        """Evaluate whether all conditions are satisfied"""
        return all(cond(facts) for cond in self.conditions)

    def fire(self, facts: dict[str, Fact]) -> list[Fact]:
        """Execute rule actions, producing new facts"""
        new_facts = []
        for action in self.actions:
            result = action(facts)
            if isinstance(result, Fact):
                new_facts.append(result)
            elif isinstance(result, list):
                new_facts.extend(result)
        return new_facts


class ForwardChainEngine:
    """Forward-Chain Reasoning Engine"""

    def __init__(self, max_iterations: int = 100):
        self.rules: list[Rule] = []
        self.max_iterations = max_iterations

    def add_rule(self, rule: Rule) -> None:
        self.rules.append(rule)
        # Sort by priority
        self.rules.sort(key=lambda r: r.priority, reverse=True)

    def reason(self, initial_facts: dict[str, Fact]) -> "ReasoningResult":
        """
        Execute forward-chain reasoning:
        1. Match all rules against initial facts
        2. Fire matched rules, producing new facts
        3. Re-match with new facts until no new facts are produced
        """
        facts = dict(initial_facts)
        trace = []
        fired_rules = set()

        for iteration in range(self.max_iterations):
            new_facts_added = False

            for rule in self.rules:
                if rule.rule_id in fired_rules:
                    continue

                if rule.evaluate(facts):
                    new_facts = rule.fire(facts)
                    fired_rules.add(rule.rule_id)

                    trace.append(RuleTrace(
                        iteration=iteration,
                        rule_id=rule.rule_id,
                        rule_name=rule.name,
                        matched_facts=[f.name for f in facts.values()],
                        produced_facts=[f.name for f in new_facts],
                    ))

                    for fact in new_facts:
                        if fact.name not in facts:
                            facts[fact.name] = fact
                            new_facts_added = True

            if not new_facts_added:
                break

        return ReasoningResult(
            facts=facts,
            trace=trace,
            iterations=iteration + 1,
        )

#3.3 ML Model Integration

The reasoning engine also supports ML model inference:

Python

class MLInferenceLayer:
    """ML Inference Layer: Integrates trained models for prediction"""

    def __init__(self, model_registry):
        self.registry = model_registry
        self._loaded_models: dict[str, Any] = {}

    async def predict(
        self,
        model_id: str,
        features: dict[str, Any],
        version: str = "latest",
    ) -> "PredictionResult":
        """Run inference using a specified model"""
        model = await self._get_model(model_id, version)
        feature_vector = self._extract_features(model, features)

        prediction = model.predict(feature_vector)
        confidence = model.predict_proba(feature_vector)

        return PredictionResult(
            model_id=model_id,
            version=version,
            prediction=prediction,
            confidence=float(confidence.max()),
            feature_importance=model.get_feature_importance(feature_vector),
        )

    async def _get_model(self, model_id: str, version: str):
        """Load model from the model registry"""
        cache_key = f"{model_id}:{version}"
        if cache_key not in self._loaded_models:
            model_meta = await self.registry.get_model(model_id, version)
            self._loaded_models[cache_key] = model_meta.load()
        return self._loaded_models[cache_key]

#4. Decide Stage: The Dual-Engine Decision Model

#4.1 DecisionEngine Overall Architecture

The Decide stage transforms reasoning results into concrete decision plans. coomia-dip's DecisionEngine employs a "dual-engine" architecture:

Code

DecisionEngine Dual-Engine Architecture:

┌──────────────────────────────────────────────────────────────┐
│                      DecisionEngine                           │
│                                                              │
│  Reasoning Results                                           │
│     │                                                        │
│     ▼                                                        │
│  ┌──────────────────┐                                        │
│  │  Decision Router  │─── Selects engine based on scenario    │
│  └────────┬─────────┘                                        │
│           │                                                   │
│     ┌─────┴─────┐                                            │
│     ▼           ▼                                            │
│  ┌──────────┐  ┌──────────────┐                              │
│  │ Decision │  │ Constraint   │                              │
│  │ Tree     │  │ Solver       │                              │
│  │ Engine   │  │ (OR-Tools)   │                              │
│  │          │  │              │                              │
│  │ Fast Path│  │ Optimization │                              │
│  │ Rule     │  │ Constraint   │                              │
│  │ Matching │  │ Solving      │                              │
│  └────┬─────┘  └──────┬───────┘                              │
│       │               │                                      │
│       └───────┬───────┘                                      │
│               ▼                                              │
│  ┌──────────────────────────┐                                │
│  │   Decision Assembler      │                                │
│  │   Assemble plan + approval│                                │
│  └──────────────────────────┘                                │
│               │                                              │
│               ▼                                              │
│  ┌──────────────────────────┐                                │
│  │   Decision Record         │                                │
│  │   Decision log + audit    │                                │
│  └──────────────────────────┘                                │
└──────────────────────────────────────────────────────────────┘

#4.2 Decision Tree Engine

The decision tree engine handles deterministic, rule-based decisions:

Python

from enum import Enum
from pydantic import BaseModel

class DecisionNodeType(str, Enum):
    CONDITION = "condition"
    ACTION = "action"
    LEAF = "leaf"

class DecisionNode(BaseModel):
    """Decision tree node"""
    node_id: str
    node_type: DecisionNodeType
    condition: str | None = None         # Condition expression
    true_branch: str | None = None       # Child node when condition is true
    false_branch: str | None = None      # Child node when condition is false
    action_type: str | None = None       # Action type for leaf nodes
    action_params: dict | None = None    # Action parameters

class DecisionTreeEngine:
    """Decision Tree Engine"""

    def __init__(self):
        self.trees: dict[str, list[DecisionNode]] = {}

    def register_tree(self, tree_id: str, nodes: list[DecisionNode]) -> None:
        self.trees[tree_id] = {n.node_id: n for n in nodes}

    def evaluate(
        self, tree_id: str, context: dict[str, Any]
    ) -> "DecisionOutcome":
        """Traverse the decision tree using context to reach a decision"""
        nodes = self.trees[tree_id]
        current = nodes["root"]
        path = []

        while current.node_type == DecisionNodeType.CONDITION:
            result = self._eval_condition(current.condition, context)
            path.append(TraceStep(
                node_id=current.node_id,
                condition=current.condition,
                result=result,
            ))

            next_id = current.true_branch if result else current.false_branch
            current = nodes[next_id]

        return DecisionOutcome(
            tree_id=tree_id,
            decision_node=current.node_id,
            action_type=current.action_type,
            action_params=current.action_params,
            trace_path=path,
        )

#4.3 Constraint Solver Engine

For optimization decisions (such as resource allocation, scheduling), OR-Tools constraint solving is used:

Python

from ortools.sat.python import cp_model

class ConstraintSolverEngine:
    """Constraint Solver Engine: Handles optimization decisions"""

    def solve_assignment(
        self,
        tasks: list[dict],
        resources: list[dict],
        constraints: list[dict],
        objective: str = "minimize_cost",
    ) -> "SolverResult":
        """Solve a resource assignment problem"""
        model = cp_model.CpModel()

        # Create decision variables
        assignments = {}
        for task in tasks:
            for resource in resources:
                var_name = f"assign_{task['id']}_{resource['id']}"
                assignments[(task["id"], resource["id"])] = model.NewBoolVar(
                    var_name
                )

        # Constraint: Each task must be assigned to exactly one resource
        for task in tasks:
            model.Add(
                sum(
                    assignments[(task["id"], r["id"])]
                    for r in resources
                ) == 1
            )

        # Constraint: Resource capacity limits
        for resource in resources:
            model.Add(
                sum(
                    assignments[(t["id"], resource["id"])] * t["demand"]
                    for t in tasks
                ) <= resource["capacity"]
            )

        # Apply custom constraints
        for constraint in constraints:
            self._apply_constraint(model, assignments, constraint)

        # Set objective function
        if objective == "minimize_cost":
            cost_terms = []
            for task in tasks:
                for resource in resources:
                    cost = self._compute_cost(task, resource)
                    cost_terms.append(
                        assignments[(task["id"], resource["id"])] * cost
                    )
            model.Minimize(sum(cost_terms))

        # Solve
        solver = cp_model.CpSolver()
        solver.parameters.max_time_in_seconds = 30
        status = solver.Solve(model)

        if status in (cp_model.OPTIMAL, cp_model.FEASIBLE):
            result_assignments = []
            for task in tasks:
                for resource in resources:
                    if solver.Value(assignments[(task["id"], resource["id"])]):
                        result_assignments.append({
                            "task_id": task["id"],
                            "resource_id": resource["id"],
                        })

            return SolverResult(
                status="optimal" if status == cp_model.OPTIMAL else "feasible",
                assignments=result_assignments,
                objective_value=solver.ObjectiveValue(),
                solve_time_ms=solver.WallTime() * 1000,
            )
        else:
            return SolverResult(
                status="infeasible",
                assignments=[],
                objective_value=None,
                solve_time_ms=solver.WallTime() * 1000,
            )

#5. Act Stage: ActionEngine Executes Decisions

#5.1 ActionEngine and 10 Executor Types

The Act stage transforms decisions into actual operations. coomia-dip's ActionEngine orchestrates 10 executor types:

Code

ActionEngine Executor Matrix:

┌─────────────────────────────────────────────────────────────┐
│                      ActionEngine                            │
│                                                             │
│  ┌────────────────┐  ┌────────────────┐  ┌───────────────┐ │
│  │ CreateObject    │  │ UpdateObject    │  │ DeleteObject  │ │
│  │ Create ontology │  │ Update object  │  │ Delete object │ │
│  │ object          │  │ properties     │  │               │ │
│  └────────────────┘  └────────────────┘  └───────────────┘ │
│                                                             │
│  ┌────────────────┐  ┌────────────────┐  ┌───────────────┐ │
│  │ CreateRelation  │  │ DeleteRelation  │  │InvokeFunction │ │
│  │ Create link     │  │ Remove link     │  │ Call UDF      │ │
│  └────────────────┘  └────────────────┘  └───────────────┘ │
│                                                             │
│  ┌────────────────┐  ┌────────────────┐  ┌───────────────┐ │
│  │ Webhook        │  │ Notification    │  │ SimpleOp      │ │
│  │ External call   │  │ Send alert     │  │ Simple op     │ │
│  └────────────────┘  └────────────────┘  └───────────────┘ │
│                                                             │
│  ┌────────────────┐                                        │
│  │ CompositeOp    │   <- Composite: orchestrates atomics    │
│  │ Transactional  │                                        │
│  │ composition    │                                        │
│  └────────────────┘                                        │
└─────────────────────────────────────────────────────────────┘

#5.2 Execution Engine Core Code

Python

from abc import ABC, abstractmethod
from enum import Enum

class ExecutorType(str, Enum):
    CREATE_OBJECT = "create_object"
    UPDATE_OBJECT = "update_object"
    DELETE_OBJECT = "delete_object"
    CREATE_RELATION = "create_relation"
    DELETE_RELATION = "delete_relation"
    INVOKE_FUNCTION = "invoke_function"
    WEBHOOK = "webhook"
    NOTIFICATION = "notification"
    SIMPLE_OP = "simple_op"
    COMPOSITE_OP = "composite_op"

class ActionExecutor(ABC):
    """Action Executor base class"""

    @abstractmethod
    async def execute(self, params: dict) -> "ActionResult":
        ...

    @abstractmethod
    async def compensate(self, params: dict, execution_id: str) -> None:
        """Compensation operation: used for Saga rollback"""
        ...

class ActionEngine:
    """Unified Action Execution Engine"""

    def __init__(self, executors: dict[ExecutorType, ActionExecutor]):
        self.executors = executors
        self.audit_log = AuditLogger()

    async def execute_action(
        self,
        action_type: ExecutorType,
        params: dict,
        decision_id: str,
        operator: str,
    ) -> "ActionResult":
        """Execute a single action"""
        executor = self.executors[action_type]

        # Take pre-execution snapshot
        snapshot = await self._take_snapshot(action_type, params)

        try:
            result = await executor.execute(params)

            # Audit log
            await self.audit_log.record(
                decision_id=decision_id,
                action_type=action_type.value,
                operator=operator,
                params=params,
                result=result,
                snapshot_before=snapshot,
                status="success",
            )

            return result

        except Exception as e:
            await self.audit_log.record(
                decision_id=decision_id,
                action_type=action_type.value,
                operator=operator,
                params=params,
                error=str(e),
                snapshot_before=snapshot,
                status="failed",
            )
            raise

#5.3 Temporal Workflow Orchestration for Long Transactions

For decisions involving multi-step operations, Temporal orchestrates a Saga pattern:

Python

from temporalio import workflow, activity
from datetime import timedelta

@workflow.defn
class DecisionExecutionWorkflow:
    """Decision Execution Workflow: Saga Pattern"""

    @workflow.run
    async def run(self, decision: dict) -> dict:
        """Execute all actions for a decision with compensation rollback"""
        executed_actions = []

        try:
            for action in decision["actions"]:
                result = await workflow.execute_activity(
                    execute_action_activity,
                    args=[action],
                    start_to_close_timeout=timedelta(minutes=5),
                    retry_policy=RetryPolicy(
                        maximum_attempts=3,
                        backoff_coefficient=2.0,
                    ),
                )
                executed_actions.append((action, result))

            return {
                "status": "completed",
                "results": [r for _, r in executed_actions],
            }

        except Exception as e:
            # Saga compensation: reverse-order rollback of executed actions
            for action, result in reversed(executed_actions):
                try:
                    await workflow.execute_activity(
                        compensate_action_activity,
                        args=[action, result["execution_id"]],
                        start_to_close_timeout=timedelta(minutes=5),
                    )
                except Exception as comp_error:
                    workflow.logger.error(
                        f"Compensation failed for {action['type']}: "
                        f"{comp_error}"
                    )

            return {
                "status": "rolled_back",
                "error": str(e),
                "compensated_actions": len(executed_actions),
            }

#6. Closed-Loop Data Flow: End-to-End Example

#6.1 Supply Chain Anomaly Detection Scenario

A complete supply chain anomaly detection scenario demonstrates the four-step loop:

Code

Supply Chain Anomaly Detection Loop Example:

Timeline ───────────────────────────────────────────────────────→

[Sense]                [Think]              [Decide]          [Act]
  │                      │                    │                 │
  │  IoT sensor reports   │  Rule engine       │  Decision tree  │  Execute
  │  Warehouse temp=35C   │  matches "high     │  determines     │  (1) Create PO
  │                      │  temp warning"     │  emergency      │  (2) Notify mgr
  │  ERP sync             │  ML model predicts │  restock needed │  (3) Adjust route
  │  Inventory = 50       │  24h stock < 20    │  Constraint     │  (4) Webhook ERP
  │                      │                    │  solver: best   │
  │  Weather API          │  Hybrid reasoning  │  supplier = B   │  (5) Approval
  │  Tomorrow 40C         │  verdict: high risk│  Urgent: auto   │     flow
  │                      │                    │  approved       │
  ▼                      ▼                    ▼                 ▼
OntologyEvent ──→ ReasoningResult ──→ DecisionOutcome ──→ ActionResult
                                                              │
                                                              │ Feedback
                                                              ▼
                                                         New Fact
                                                      (back to Sense)

#6.2 End-to-End Code Integration

Python

async def supply_chain_decision_loop(
    sense: SenseLayer,
    reasoning: ReasoningEngine,
    decision: DecisionEngine,
    action: ActionEngine,
):
    """Supply chain intelligent decision loop"""

    # -- Sense --
    temperature_event = await sense.ingest_event(
        "iot_warehouse_01",
        {"sensor_id": "temp_01", "value": 35.0, "unit": "celsius"},
    )
    inventory_event = await sense.ingest_event(
        "erp_system",
        {"product_id": "SKU_001", "quantity": 50, "warehouse": "WH_01"},
    )

    # Compute derived properties
    derived = await sense.derived_property_service.compute_derived(
        "Warehouse", "WH_01", {"temperature", "inventory_level"}
    )

    # -- Think --
    facts = {
        "temperature": Fact("temperature", 35.0, "sensor"),
        "inventory": Fact("inventory", 50, "erp"),
        "risk_level": Fact("risk_level", derived["risk_score"], "derived"),
    }
    reasoning_result = await reasoning.reason(facts, strategy="hybrid")

    # -- Decide --
    decision_outcome = await decision.evaluate(
        tree_id="supply_chain_emergency",
        context={
            "risk_level": reasoning_result.facts["risk_level"].value,
            "predicted_shortage": reasoning_result.facts.get(
                "predicted_shortage", Fact("predicted_shortage", True, "ml")
            ).value,
        },
    )

    # -- Act --
    if decision_outcome.action_type == "emergency_replenishment":
        # Constraint solving: select optimal supplier
        solver_result = await decision.constraint_solver.solve_assignment(
            tasks=[{"id": "replenish_SKU001", "demand": 100}],
            resources=[
                {"id": "supplier_A", "capacity": 50, "cost": 10},
                {"id": "supplier_B", "capacity": 200, "cost": 8},
            ],
            constraints=[],
            objective="minimize_cost",
        )

        # Execute a series of actions
        await action.execute_action(
            ExecutorType.CREATE_OBJECT,
            {"object_type": "PurchaseOrder", "properties": {
                "supplier": solver_result.assignments[0]["resource_id"],
                "quantity": 100,
                "priority": "urgent",
            }},
            decision_id=decision_outcome.decision_id,
            operator="system:auto",
        )

        await action.execute_action(
            ExecutorType.NOTIFICATION,
            {"channel": "dingtalk", "template": "urgent_replenishment",
             "recipients": ["warehouse_manager"]},
            decision_id=decision_outcome.decision_id,
            operator="system:auto",
        )

#7. Closed-Loop Feedback Mechanism

#7.1 Decision Effectiveness Evaluation

The key to a closed loop is the "closed" part -- execution results must feed back to the sense layer:

Python

class FeedbackCollector:
    """Decision Feedback Collector"""

    async def collect_outcome(
        self, decision_id: str, metrics: dict
    ) -> "FeedbackRecord":
        """Collect effectiveness metrics after decision execution"""
        original_decision = await self.decision_store.get(decision_id)

        feedback = FeedbackRecord(
            decision_id=decision_id,
            expected_outcome=original_decision.expected_outcome,
            actual_outcome=metrics,
            deviation=self._compute_deviation(
                original_decision.expected_outcome, metrics
            ),
            timestamp=datetime.utcnow(),
        )

        # Inject feedback as new fact into the Sense layer
        await self.sense_layer.ingest_event(
            "feedback_system",
            {
                "type": "decision_feedback",
                "decision_id": decision_id,
                "effectiveness": feedback.deviation,
            },
        )

        return feedback

#7.2 Rule Self-Optimization

Code

Feedback-Driven Rule Optimization Loop:

┌──────────┐     ┌──────────┐     ┌──────────┐
│ Decision │────→│ Effect   │────→│ Rule     │
│ Records  │     │ Evaluate │     │ Tuning   │
│          │     │          │     │          │
│ 300+     │     │ Deviation│     │ Threshold│
│ decisions│     │ Analysis │     │ Adjust   │
│          │     │ 85% rate │     │ Weight   │
└──────────┘     └──────────┘     └──────┬───┘
                                        │
                                        ▼
                                  ┌──────────┐
                                  │ A/B Test  │
                                  │ New rules │
                                  │ vs old    │
                                  └──────────┘

#8. Performance and Observability

#8.1 Latency Budget per Stage

Stage	Target Latency	Actual P99	Key Optimization
Sense	< 50ms	32ms	Event batching, async derived computation
Think	< 200ms	145ms	Rete network caching, model preloading
Decide	< 100ms	78ms	Decision tree pruning, solver timeout
Act	< 500ms	320ms	Async execution, batch writes
End-to-End	< 1s	~600ms	Pipeline parallelism

#8.2 Observability Metrics

Python

# Closed-loop monitoring metrics
STDA_METRICS = {
    "sense_events_total": Counter("sense_events_total", "Total sense events"),
    "think_reasoning_duration": Histogram(
        "think_reasoning_duration_seconds", "Reasoning duration distribution"
    ),
    "decide_outcome_total": Counter(
        "decide_outcome_total", "Decision outcome count", ["outcome_type"]
    ),
    "act_execution_total": Counter(
        "act_execution_total", "Action execution count", ["executor_type", "status"]
    ),
    "feedback_deviation": Histogram(
        "feedback_deviation", "Decision effectiveness deviation distribution"
    ),
    "loop_e2e_duration": Histogram(
        "stda_loop_duration_seconds", "End-to-end loop duration"
    ),
}

#9. Comparison with Palantir Foundry

Dimension	Palantir Foundry	coomia-dip
Reasoning Engine	Proprietary Logic Engine	Open-source ReasoningEngine (forward chain + ML)
Decision Engine	Built-in AIP Logic	DecisionEngine (decision tree + OR-Tools)
Execution Engine	Actions Framework	ActionEngine (10 executor types)
Workflow	Built-in engine	Temporal (cloud-native, extensible)
Explainability	Limited	Full reasoning chain + audit log
User Functions	TypeScript	Python/TS/Groovy/WASM/Kotlin
Open Source	No	Yes

#Key Takeaways

The STDA loop (Sense -> Think -> Decide -> Act) is the core architectural pattern for coomia-dip intelligent decision-making
The Sense layer semanticizes raw data through Ontology events and DerivedPropertyService
The Think layer's ReasoningEngine supports hybrid forward-chain reasoning and ML inference
The Decide layer's DecisionEngine provides a decision tree fast path and OR-Tools constraint solving dual engine
The Act layer's ActionEngine orchestrates 10 executor types with Temporal Saga compensation
Closed-loop feedback injects execution results back into the sense layer, driving rule self-optimization
End-to-end latency stays under 1 second, meeting real-time decision requirements

#Next Article

Next up: S5-02 Rule Engine Design: Forward-Chain Reasoning Principles and Implementation will deep-dive into the ReasoningEngine's Rete network implementation, fact matching algorithms, and rule firing mechanics.

tags: #intelligent-decision #STDA-loop #reasoning-engine #decision-engine #action-engine #coomia-dip #enterprise-intelligence