Trustworthy & Reliable AI

Plan Summary

Provide a structured breakdown of the agent's reasoning and approach - showing goal interpretation, strategy, subtask checklist, and assumptions - so users can evaluate the plan before execution begins.

What is Plan Summary?

While Intent Preview shows WHAT the agent will do, Plan Summary explains WHY and HOW. When an agent breaks a complex goal into subtasks, users need to understand the agent's reasoning - not just its intended actions. This is especially critical for knowledge work where there are multiple valid approaches. 'Research competitor pricing' could mean scraping websites, reading analyst reports, or checking public databases - the strategy matters as much as the outcome. The Plan Summary provides goal interpretation, strategy explanation, a subtask checklist that updates in real-time, explicit assumptions the user can correct, and resource and time estimates. This pattern extends explainability from retrospective ('here's why I gave this answer') to prospective ('here's why I'm taking this approach').

Problem

While Intent Preview shows what the agent will do, users also need to understand why and how. When an agent breaks a complex goal into subtasks, users can't evaluate whether the approach is sound without seeing the reasoning and assumptions behind the plan.

Solution

Provide a structured plan summary with goal interpretation, strategy explanation, a subtask checklist with real-time progress, explicit editable assumptions, and resource/time estimates. Keep it concise by default with full reasoning available on expansion.

Real-World Examples

Implementation

AI Design Prompt

Guidelines & Considerations

Implementation Guidelines

1

Keep the plan summary concise by default - full reasoning available on expansion. Most users want the 3-line version, not the 30-line version.

2

Surface assumptions as interactive elements - users should be able to correct assumptions before execution begins.

3

Update the plan in real-time during execution. Show completed steps, current step, and remaining steps with a progress indicator.

4

When the agent deviates from the original plan due to unexpected findings, highlight the deviation and explain why.

5

Allow users to save good plans as templates for future similar tasks.

6

Include a goal interpretation section so users can verify the agent understood their request correctly before work begins.

7

Show resource and time estimates upfront to set expectations for complex multi-step operations.

Design Considerations

1

Plan modification rate: how often users change the approach before execution indicates plan quality

2

Plan deviation rate: how often the agent needs to deviate from the stated plan during execution

3

Assumption correction rate: how often users correct stated assumptions indicates the right things are being surfaced

4

Balancing plan detail with readability - too much detail overwhelms, too little fails to build confidence

5

Real-time plan updates must be non-disruptive - the user should see progress without being interrupted

6

Templates from saved plans must handle variation in new tasks without forcing rigid adherence

7

Plans for knowledge work are inherently uncertain - communicate this without undermining trust

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About the author

Imran Mohammed is a product designer who studies how the best AI products are designed. He studies and documents AI/UX patterns from shipped products (36 and counting) and is building Gist.design, an AI design thinking partner. His weekly analysis reaches thousands of designers on Medium.