⚡ Systems That Plan Their Own Path

Agentic Planning vs Predetermined Flows

Traditional interfaces require designers to anticipate every possible user path. Agentic systems flip the script - they maintain awareness of your underlying objectives and dynamically construct interaction patterns to achieve them.

Traditional Flow

Predetermined Paths

TravelBooker Pro

"Help me plan a vacation"

Select Destination

Choose from predefined list of locations

Pick Travel Dates

Use calendar widget to select dates

Choose Budget Range

Select from predefined price tiers

Select Activities

Check boxes for available activities

Review & Book

Confirm selections and complete purchase

What happens when goals change?

🚫 Must restart from step 1

🚫 Can't adapt to new information

🚫 No understanding of why changes occurred

🚫 Can't suggest alternatives when constraints conflict

Agentic System

Dynamic Adaptation

AI Travel Companion

You: "Help me plan a vacation"

Understanding your underlying goals...

I'll gather context as we explore options together, adapting the process to your evolving preferences.

AI: "I'd love to help! Tell me what's driving this trip - is it relaxation, adventure, family time, or something else entirely?"

You: "My partner and I need to reconnect after a stressful year. Somewhere peaceful but not boring."

AI: "Perfect - I'm thinking destinations that blend tranquility with meaningful experiences. What's your sense of ideal pace? And any time of year that would be especially restorative for you both?"

Real-time adaptation capabilities:

✅ Understands 'why' behind preferences

✅ Adapts when new constraints emerge

⚡ Reconfigures options when conflicts arise

✅ Suggests creative solutions to trade-offs

How Goals Evolve During Planning

Agentic systems adapt to changing user understanding and constraints

Initial Goal

"Plan a vacation"

Refined Goal

"Reconnect peacefully"

Contextual Goal

"Recharge together"

Actionable Goal

"Tuscany + cooking class"

Discovery Phase

System explores underlying motivations and constraints through natural conversation.

Techniques: Open-ended questions, emotional context gathering, preference mapping

Refinement Phase

Goals become more specific as the system understands personal context and priorities.

Techniques: Clarifying questions, trade-off exploration, priority ranking

Contextualization

System incorporates external constraints, relationships, and situational factors.

Techniques: Constraint integration, relationship consideration, timing optimization

Actualization

Goals transform into specific, actionable plans that align with discovered values.

Techniques: Concrete planning, resource allocation, timeline creation

Constraint Resolution in Action

How agentic systems handle conflicting requirements and changing priorities

Scenario: Budget Constraint Emerges Mid-Planning

Initial

Perfect match found: Luxury Tuscany villa with private chef

Meets all emotional and experiential goals

Constraint

New information: Partner mentions budget concerns

Cost suddenly becomes a critical factor

Adaptation

System reconfigures without losing core goals

🏠 Smaller villa + local cooking class

🍝 Market tours + home cooking experience

📅 Slightly longer stay for better value

Result

Enhanced solution that's both affordable and meaningful

Deeper local immersion + better value + original goals preserved

Traditional System Response

❌ "Your budget is too low for this destination"

❌ Forces user to restart search process

❌ Suggests generic budget alternatives

❌ Loses all context about underlying goals

Agentic System Response

✅ Preserves core emotional and experiential goals

✅ Finds creative ways to achieve objectives within budget

✅ Often discovers better solutions through constraint

✅ Maintains momentum and user engagement

Agentic Planning in Current Systems

Examples of goal-alignment mechanisms already transforming user experiences

Spotify Discover Weekly

Doesn't ask "what genre do you want?" Instead, it observes behavior patterns and dynamically constructs musical journeys that expand your taste horizons.

Behavioral Learning

ChatGPT Conversations

Adapts its approach based on your communication style and goals, rather than following scripted support flows. Maintains context across complex problem-solving.

Contextual Adaptation

Tesla Navigation

Considers battery level, traffic, charging station availability, and driver patterns to dynamically route trips, adapting to changing conditions in real-time.

Dynamic Optimization

Designing for Agentic Planning

🎯

Focus on Goal Alignment, Not Flow Completion

Design systems that understand and serve user objectives rather than guiding them through predetermined steps.

🔄

Enable Dynamic Path Construction

Build flexibility into your system architecture so paths can be reconfigured based on evolving user needs and constraints.

💬

Prioritize Conversational Discovery

Use natural dialogue to understand context, motivations, and constraints rather than rigid form-based data collection.

⚡

Design for Graceful Constraint Handling

When conflicts arise, help users find creative solutions that preserve their underlying goals rather than forcing compromises.

← Previous: Relationship Architecture Next: Success Metrics →