Overview

The Entity Execution Engine (E3) is a platform-layer orchestration system that enables continuous, multi-agent execution over entities as they evolve through time.

E3 is an abstraction over Dialectic Chains & AI Agents. in Wity ↗.

At its core, the engine allows high-level intents, states, or goals to be operationalized into intelligent, automatable transformations on entities—producing new entity versions, signals, artifacts, or actions via agents.

E3 allows running continuous AI execution over real-world entities. Instead of triggering one-off jobs or workflows, the engine treats each entity as an evolving execution surface - where AI agents observe entity-state through signals, make decisions, and execute actions to transform the entity-state over time, creating a closed-loop system.

Each entity runs its own ai system, governed by policies, execution constraints, and feedback signals.

Core Idea

An Entity is a persistent identity.
An Entity Version is its state at a point (or span) in time.
An Engine defines how entities evolve by executing agents that transform them into desired states.

The Entity Execution Engine provides:

• A manifest that declares what transformations are possible
• An engine runtime that decides when and how to execute them
• A cell-based execution model that binds entities × states × agents.

Conceptual Model

1. Entity Evolution Manifest

A declarative blueprint that defines:

• Applicable entity types or concepts
• Valid target states
• Allowed transitions between states
• Constraints, affordances, and semantics

Think of it as:

“The grammar of how this class of entities is allowed to evolve.”

The manifest is static, versionable, and shared.

2. Entity Evolution Engine

An instantiated runtime of a manifest that:

• Binds the manifest to real entities
• Configures execution behavior
• Owns scheduling, triggering, and orchestration logic

Each engine instance operates on many entities over time, not just one.

3. Engine Cells (Execution Units)

An Engine Cell represents a single executable transformation:

Each cell defines:

• Which agent runs
• With what seed inputs & parameters
• Under what temporal or conditional rules
• Producing which entity versions, signals, or artifacts

Cells are embedded within an engine and evaluated as part of its execution loop.

This Is Not a State Machine

Traditional state machines:

• Operate on one entity
• Assume linear or finite transitions
• Are usually reactive

The Entity Execution Engine instead:

• Operates across many entities simultaneously
• Supports multi-dimensional state spaces
• Allows parallel, conditional, and recurring execution
• Treats states as targets, not just transitions

Execution Flow (High-Level)

1. Entity exists with a current version
2. Engine evaluates applicable cells
3. Conditions / schedules are satisfied
4. Agent executes
5. New entity version (or artifact / signal) is produced
6. Events and links are recorded
7. Entity continues evolving

This loop can run:

• Continuously
• Periodically
• Event-triggered
• Or hybrid

Real-World Implementations

1. Product Creative AI Engine (for D2C Brands)

Objective

“Automate image & creative production at scale for each SKU across PDPs, catalogs, ads, and more.”

How E3 is used

• Entity: product.sku
• Entity Versions: creative-ready states (flat-lay, lifestyle, ad-variant, etc.)
• Engine Cells:
  • SKU × “Flat Lay Image” → Image Generation Agent
  • SKU × “Catalog Creative” → Layout / Design Agent
  • SKU × “Ad Variant” → Creative Remix Agent
• Execution:
  • Runs continuously as SKUs, seasons, or campaigns change

Result:

• Each SKU becomes a living entity with continuously evolving creatives.

2. Subscriber Execution AI Engine (for MVNOs / Telcos)

Objective

“Continuously running AI system on each subscriber that executes support, engagement, monetization, and value-added services.”

How E3 is used

• Entity: subscriber
• Entity Versions: engagement / lifecycle states
• Engine Cells:
  • Subscriber × “Support Needed” → Support Agent
  • Subscriber × “Churn Risk” → Retention Agent
  • Subscriber × “Upsell Opportunity” → Monetization Agent
• Execution:
  • Event-driven + periodic (usage, tickets, signals)

Result:

• Each subscriber is managed by a persistent AI execution fabric, not tickets or flows.

What this enables

Wity E3 turns AI agents into long-running operators on real & evolving entites:

• Long-lived AI processes, rather than one-shot executions
• Composable intelligence graphs, not monolithic logic
• Event-driven and periodic execution, rather than batch-only runs
• Entity-centric AI, not request-centric AI