🐾 A Query Plan for Your Business: How DOGs Mirror Database Architectures

After our last blog posts on Agentic Data Object Graphs (DOGs), we’ve received some great questions about the fundamentals of how they work. So, let’s break it down using a familiar analogy—traditional database architecture.

Just as databases efficiently process queries, PackRunner's Agentic Product Architecture transforms business intent into executable, AI-driven workflows.

🔍 From SQL to Agentic AI: The Structural Parallels

Consider how databases and PackRunner take an input request, optimize execution, and return actionable results:

🖥️ Input Layer: Receiving the Request

📊 Traditional Database:

• Accepts SQL queries from analysts, BI tools, applications, etc.
• Queries specify what data to retrieve and how to manipulate it.

🤖 PackRunner (DOGs):

• Takes business process instructions from LLMs, AI Agents, business analysts, and applications.
• Requests describe business intent, not just data retrieval.

📌 Key Difference?

Traditional databases retrieve data based on predefined queries, while PackRunner interprets business intent and dynamically constructs workflows that combine AI, automation, and data-driven decisions. Instead of just answering questions, DOGs take action.

🛠️ Translation Phase: Converting Intent into an Executable Plan

📊 Traditional Database:

• A Query Parser breaks SQL into an execution plan, optimized for database tables and indexes.

🤖 PackRunner (DOGs):

• An AI/Agentic Translator transforms business intent into data product orchestration:
  ✅ Identifies relevant AI models, decision logic, and data flows
  ✅ Maps connections to business processes
  ✅ Creates metadata-rich Data Product Containers to interact with underlying business data

📌 Key Difference?

Traditional databases optimize queries for static data retrieval, whereas DOGs dynamically assemble executable workflows from AI and data products.

📑 Planning Phase: Structuring Execution

📊 Traditional Database:

• A Query Planner optimizes execution using joins, filters, and groupings.

🤖 PackRunner (DOGs):

• Constructs a queryable, executable Data Object Graph (DOG)
  ✅ Nodes represent AI agents, models, decision points, and business logic
  ✅ Edges define dependencies & data transformations
  ✅ Runtime-aware: dynamically adapts execution based on system state

📌 Key Difference?

Unlike a database, PackRunner is not just retrieving data—it is executing interconnected business processes in real time.

🚀 Execution Phase: Running the Workflow

📊 Traditional Database:

• The Query Executor retrieves data from tables, executes joins & aggregations, and returns the result.

🤖 PackRunner (DOGs):

• The Data Object Graph is traversed dynamically, executing each Data Product in sequence:
  ✅ Data flows between nodes as AI models, rules engines, and transaction updates are executed.
  ✅ Each node can act independently—running microservices, executing ML models, or triggering process automation.
  ✅ Final outputs can be email alerts, database updates, or real-time decision insights.

📌 Key Difference?

Traditional databases return static datasets—DOGs return real-time actions & automated business decisions.

💡 Why This Matters: The Evolution from Data to Execution

Unlike traditional databases that query static data, PackRunner creates living, executable workflows that orchestrate real business processes across sales, finance, risk, and operations.

🔥 Key Advantages of DOGs Over Databases:

✅ Dynamic, AI-powered workflows vs. static queries
✅ Distributed execution across business domains
✅ Real-time process orchestration vs. batch data retrieval
✅ Seamless integration with AI models, automation, and business apps

🚀 This is how PackRunner turns business logic into executable reality.

What’s your take? Could your business benefit from a living, executable data architecture? Reach out to us and learn how we can help you.