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The AI Interaction Lifecycle

Modern AI experiences can range from simple conversational interactions to fully autonomous agents capable of completing multi-step tasks. Regardless of the level of autonomy, AI interactions often follow a common lifecycle that guides users from expressing intent to achieving an outcome.

The AI Interaction Lifecycle defines six stages that support both informational and agentic experiences:

  1. Express Intent
  2. Display Results
  3. Refine Output
  4. Take Action
  5. Execute & Monitor
  6. Summary

Each stage represents a distinct phase in the interaction between a user and an AI system, from defining a goal to reviewing the final outcome.

The Six Stages at a Glance

Stage Primary Question Actions Taken 
1. Express Intent
 What does the user want? The user communicates a goal, question, or request. 
2. Display Results
 What did the AI produce? The AI presents generated outputs, recommendations, explanations, and supporting information. 
3. Refine Output
 How does the user improve the result? The user iteratively improves, redirects, or adjusts generated results until they meet their needs. 
4. Take Action
 What should happen next? The AI transitions from generating information to planning and preparing actions. 
5. Execute & Monitor
 How is it being carried out? The AI actively performs work while users monitor progress and maintain appropriate oversight. 
6. Summary
 What happened? The AI communicates outcomes, accomplishments, decisions, supporting evidence, and recommended next steps.

Principles Matrix

The following matrix shows which AI UX principles are most closely associated with each stage of the AI Interaction Lifecycle.

Principle Express Intent Display Results Refine Output Take Action Execute & Monitor Summary
Control ✓ ✓ ✓ ✓ ✓
Transparency ✓ ✓ ✓ ✓
Human Override ✓
Trust & Confidence ✓ ✓ ✓

Lifecycles for Different AI Capabilites

Informational AI Lifecycle

Informational AI experiences focus on helping users discover information, generate content, explore ideas, answer questions, and produce insights.

Examples include:
  • Conversational assistants
  • Search and research experiences
  • Content generation
  • Brainstorming and ideation
  • Knowledge retrieval
  • Summarization

These experiences typically operate within the first three stages of the lifecycle. The interaction focuses on generating, understanding, and improving information or content.

Informational AI Lifecycle

Agent-Assisted AI Lifecycle

Agent-assisted experiences extend beyond information delivery and enable AI systems to plan, execute, and monitor work on a user's behalf.

Examples include:
  • AI agents
  • Workflow automation
  • Coding assistants
  • Research agents
  • Operational copilots
  • Business process automation

These experiences build upon the Informational AI Lifecycle and introduce three additional stages. The interaction expands from generating information to planning, executing, monitoring, and reporting on actions.

Informational AI Lifecycle