How to use Outcome, Role and Interaction (ORI) to design AI Agents for your organization
In the rapidly evolving landscape of artificial intelligence, designing effective agent workflow systems requires a structured methodology that balances complexity with predictability. The ORI (Outcome > Role > Interaction) design approach offers a systematic framework for architecting AI agent workflows that are both powerful and practical.
Understanding the ORI Design Hierarchy
The ORI design approach follows a hierarchical structure where each element builds upon the previous:
Outcome: The desired result or objective of the agent system
Role: The specific functions and responsibilities assigned to each agent
Interaction: The protocols and patterns governing how agents communicate and collaborate
This hierarchy ensures that design decisions are driven by clear objectives rather than technological capabilities alone.
Outcome-based design
There are essentially only two outcome-based designs when you design AI agents.
Organizations typically start their AI agent journey with task-based agents due to their lower complexity and higher reliability.
Task-based agents
Task-based agents represent the foundation of practical AI implementation in most organizations. Their popularity stems from several key advantages:
Predictability: Well-defined task boundaries make behavior more deterministic
Measurability: Clear success criteria for task completion
Scalability: Easier to replicate and deploy across different contexts
Maintenance: Simpler to debug and optimize
A prime example of task-based architecture is the Monitoring Agent pattern. These agents are designed to:
Continuously observe specific metrics or systems
Process incoming data against predefined criteria
Trigger appropriate responses or alerts
Maintain audit trails of their observations and actions
Goal-seeking agents
Goal-seeking agents represent a more sophisticated approach to agent design, suitable for complex scenarios where the path to the desired outcome isn't clearly defined. These agents:
Operate with greater autonomy
Adapt to changing environmental conditions
Make decisions based on multiple variables
Collaborate dynamically with other agents
Key characteristics of goal-seeking agents include:
Dynamic Strategy Formation: Agents can formulate and adjust their approaches based on environmental feedback
Multi-Agent Collaboration: Complex goal achievement often requires coordinated effort among multiple specialized agents
Environmental Awareness: Continuous monitoring and adaptation to changing conditions
Learning Capabilities: Improvement of strategies based on past experiences
Here is an advanced type of goal-seeking agent which is useful for complex scenario use cases where the environment variables are unpredictable
Role-based design
Coming soon..
Interaction-based design
Coming soon...
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