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:

1. Outcome: The desired result or objective of the agent system

2. Role: The specific functions and responsibilities assigned to each agent

3. 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

Task based agents are going to be Organization favorites

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

A type of task based agent : Monitoring Agent

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

Goal seeking Agent

Key characteristics of goal-seeking agents include:

1. Dynamic Strategy Formation: Agents can formulate and adjust their approaches based on environmental feedback

2. Multi-Agent Collaboration: Complex goal achievement often requires coordinated effort among multiple specialized agents

3. Environmental Awareness: Continuous monitoring and adaptation to changing conditions

4. 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

Simulation Agent, a type of Goal seeking Agent

Role-based design

Coming soon..

Interaction-based design

Coming soon...