name	foundations-problem-solution-fit
description	Problem validation and solution design. Use when discovering customer problems, generating solution hypotheses, or defining MVP scope.

Problem-Solution Fit Agent

Overview

The Problem-Solution Fit Agent validates that you're solving a real, valuable problem with the right solution approach. This agent merges Problem Framing, Alternative Analysis, Solution Building, and Innovation Strategy to ensure strong problem-solution alignment before significant investment.

Primary Use Cases: Problem discovery, solution validation, MVP definition, innovation strategy, pivot assessment.

Lifecycle Phases: Discovery (primary), Definition, major pivots, product expansion.

Core Functions

1. Problem Discovery

Identify, validate, and prioritize customer problems to ensure solving high-value pain points.

Workflow:

Identify Problems Using Jobs-to-be-Done Framework
- Functional Jobs: What tasks are customers trying to complete?
- Emotional Jobs: How do customers want to feel? What anxieties to avoid?
- Social Jobs: How do customers want to be perceived by others?
- Map current workflow and identify friction points
Measure Pain Frequency
- Daily: Problem occurs every day
- Weekly: Problem occurs 1-4 times per week
- Monthly: Problem occurs 1-4 times per month
- Quarterly: Problem occurs occasionally
- Higher frequency = higher awareness and urgency
Assess Pain Intensity
- 1 - Minor annoyance: Tolerable, low willingness to pay
- 2 - Noticeable frustration: Aware but not urgent
- 3 - Significant problem: Actively seeking solutions
- 4 - Major pain point: High urgency, budget allocated
- 5 - Critical/existential: Business-critical, will pay premium
Validate Through Research
- User Interviews: Minimum 10-15 interviews in target segment
  - Ask: "Tell me about the last time you experienced [problem]"
  - Probe: "How did you handle it? What did it cost you?"
  - Avoid: "Would you use a solution that does X?" (leading question)
- Observational Studies: Shadow users in their natural environment
- Data Analysis: Support tickets, review mining, search query data
Prioritize Problems
- Severity Score: Frequency × Intensity
- Solvability Assessment: Technical feasibility, cost to solve, time to market
- Strategic Fit: Aligns with company vision, capabilities, market position
- Problem Stack Rank: Top 3-5 problems to pursue

Output Template:

Validated Problem Stack Rank

1. [Problem Statement]
   ├── Job-to-be-Done: [functional/emotional/social job]
   ├── Frequency: [daily/weekly/monthly/quarterly]
   ├── Intensity: X/5
   ├── Severity Score: XX (frequency × intensity)
   ├── Current Cost: $X per [time period] or X hours per [time period]
   ├── Evidence: [interview quotes, data points, observations]
   ├── Solvability: [high/medium/low] (rationale)
   └── Priority: 1 (recommended focus)

2. [Problem Statement]...
3. [Problem Statement]...

Problem Selection Rationale:
[1-2 sentences explaining why problem #1 is the right focus]

Red Flags Identified:
- [Any problems that seem low-value or unsolvable]
- [Customer segments where problem doesn't exist]

2. Solution Hypothesis

Generate and evaluate multiple solution approaches to find optimal problem-solution fit.

Workflow:

Generate Multiple Solution Approaches
- Divergent Thinking: Generate 5-10 different solution concepts
- Constraint Relaxation: What if budget/time/tech weren't constraints?
- Analogy Mining: How do other industries solve similar problems?
- User Co-Creation: Involve customers in solution ideation
Evaluate Technical Feasibility
- Existing Technology: Can be built with current tech stack
- Emerging Technology: Requires new but available technology
- Research Required: Needs R&D or breakthroughs
- Impossible Today: Not feasible with current technology
Assess Effort vs Impact
- Effort: S (small - days), M (medium - weeks), L (large - months)
- Impact: Low (nice-to-have), Medium (meaningful improvement), High (10x better)
- Prioritization Matrix: High impact + Low effort = Quick wins
Evaluate Build vs Buy vs Partner
- Build: Core differentiation, IP ownership, full control
- Buy: Commodity feature, faster time-to-market, proven solution
- Partner: Complementary capabilities, shared risk, ecosystem play
Prototype and Test
- Low-Fidelity Mockups: Sketches, wireframes, storyboards
- Concept Testing: Present concepts to users, gather feedback
- Wizard of Oz: Manual process behind automated facade
- Concierge MVP: High-touch service to validate value before automation

Output Template:

Solution Hypothesis Evaluation

Problem Being Solved: [Problem #1 from stack rank]

Solution Concepts (Top 3):

Concept A: [Solution Name]
├── Description: [1-2 sentences]
├── Technical Feasibility: [existing/emerging/research/impossible]
├── Effort: [S/M/L] - [X weeks/months]
├── Impact: [Low/Medium/High] - [expected improvement]
├── Build/Buy/Partner: [decision + rationale]
├── Differentiation Potential: [low/medium/high]
├── Prototype Approach: [mockup/concept test/wizard of oz/concierge]
└── Validation Criteria: [What must be true for this to work?]

Concept B: [Solution Name]...
Concept C: [Solution Name]...

Recommended Solution: Concept [A/B/C]
Rationale: [Why this concept beats alternatives]

Next Steps:
1. [First validation experiment]
2. [Second validation experiment]
3. [MVP scoping if validation succeeds]

3. Alternative Analysis

Catalog and analyze existing solutions to identify competitive advantage opportunities.

Workflow:

Catalog Current Solutions
- Direct Competitors: Same problem, similar solution
- Indirect Competitors: Same problem, different solution
- Workarounds: Manual processes, hacks, duct-tape solutions
- Non-Consumption: People who have problem but don't solve it
Assess Customer Satisfaction
- Satisfaction Score: 1 (very dissatisfied) to 5 (very satisfied)
- Net Promoter Score: Likelihood to recommend current solution
- Review Mining: Extract common complaints and praises
- Churn/Retention Data: Why do users leave or stay?
Identify Switching Barriers
- Financial: Sunk costs, contracts, switching fees
- Technical: Data migration, integration complexity, learning curve
- Organizational: Process changes, stakeholder buy-in, training
- Psychological: Loss aversion, status quo bias, risk perception
Map Unmet Needs
- Feature Gaps: What do users wish existed?
- Performance Gaps: What's too slow, expensive, or complex?
- Experience Gaps: Where is UX frustrating or confusing?
- Integration Gaps: What doesn't connect that should?
Determine Adoption Triggers
- What event would make someone switch?: New role, company growth, regulation change
- Migration Paths: How to move users from alternative to your solution
- Value Gaps: How much better must you be to justify switching? (10x rule)

Output Template:

Alternative Analysis

Existing Alternatives (Top 5):

1. [Alternative Name/Category]
   ├── Type: [direct competitor/indirect/workaround/non-consumption]
   ├── Satisfaction: X/5 (evidence: [reviews/NPS/churn])
   ├── Strengths: [What they do well]
   ├── Weaknesses: [Where they fall short]
   ├── Switching Barriers: [financial/technical/organizational/psychological]
   ├── Market Share: X% or [dominant/emerging/niche]
   └── Unmet Needs: [What users still complain about]

2. [Alternative Name/Category]...

Competitive Advantage Opportunities:

1. [Opportunity]: [Description]
   - Why Alternative Fails Here: [reason]
   - Our Advantage: [capability/insight/approach]
   - Barrier to Replicate: [why hard for competitors to copy]

2. [Opportunity]...
3. [Opportunity]...

Adoption Strategy:
├── Adoption Trigger: [event/pain point that creates urgency]
├── Migration Path: [how to move users from alternative]
├── Required Superiority: [10x better on dimension X]
└── Early Adopter Profile: [who switches first]

Switching Cost Mitigation:
- [How to reduce financial barriers]
- [How to reduce technical barriers]
- [How to reduce organizational barriers]

4. MVP Definition

Define minimum viable product scope with clear success metrics and development priorities.

Workflow:

Determine Feature Categories
- Core Features: Must-have for MVP, solves primary problem
- Nice-to-Haves: Valuable but not essential for first version
- Non-Features: Explicitly out of scope for MVP (but maybe later)
Map Features to Problems
- Each core feature must solve a validated problem
- Avoid "cool tech" or "nice UX" without problem linkage
- Test: "If we remove this feature, can we still solve the core problem?"
Create User Stories
- Format: "As a [user type], I want [action] so that [benefit]"
- Include: Acceptance criteria, edge cases, error states
- Estimate: Story points or t-shirt sizing (S/M/L)
Estimate Development Effort
- Small: 1-3 days, low technical risk, clear requirements
- Medium: 1-2 weeks, moderate risk, some unknowns
- Large: 2+ weeks, high risk, significant unknowns or dependencies
- Total MVP timeline should be 4-12 weeks max
Assess Technical Risk
- Low Risk: Proven technology, team has experience
- Medium Risk: New to team but proven elsewhere
- High Risk: Cutting edge, uncertain feasibility, no prior art
- Flag dependencies: APIs, third-party services, integrations
Define Success Metrics
- Activation: % users who complete key action
- Engagement: Frequency of use, time spent
- Retention: % users active after 1 week, 1 month
- Satisfaction: NPS, CSAT, or qualitative feedback
- Business Metric: Revenue, conversions, or strategic goal

Output Template:

MVP Specification

Core Features (Must-Have):

1. [Feature Name]
   ├── Solves: [Problem from stack rank]
   ├── User Story: As a [user], I want [action] so that [benefit]
   ├── Acceptance Criteria: [What defines "done"]
   ├── Effort: [S/M/L] - [X days/weeks]
   ├── Technical Risk: [Low/Medium/High]
   ├── Dependencies: [APIs, services, other features]
   └── Priority: P0 (must have for launch)

2. [Feature Name]...

Nice-to-Haves (Post-MVP):
- [Feature]: [Why valuable but not essential]
- [Feature]: [Why valuable but not essential]

Explicit Non-Features:
- [Feature]: [Why explicitly out of scope]
- [Feature]: [Why explicitly out of scope]

MVP Timeline:
├── Total Effort: X weeks
├── High-Risk Items: [features requiring de-risking]
├── Critical Path: [feature A] → [feature B] → [launch]
└── Launch Date Target: [date or week]

Success Metrics:
├── Activation: X% complete [key action]
├── Engagement: X% use [frequency]
├── Retention: X% active after 1 week
├── Satisfaction: NPS > X or [qualitative threshold]
└── Business Goal: [revenue/conversions/strategic metric]

Pivot Triggers:
- If activation < X%, reconsider [assumption]
- If retention < X%, problem not painful enough
- If satisfaction < X%, solution doesn't fit problem

5. Innovation Strategy

Identify unique insights and defensible advantages to create 10x better solutions.

Workflow:

Identify 10x Improvement Opportunities
- 10x Faster: What takes hours could take seconds?
- 10x Cheaper: What's expensive could be affordable?
- 10x Easier: What's complex could be simple?
- 10x More Accessible: Who's excluded could be included?
Uncover Unique Insights
- Contrarian Beliefs: What do you believe that others don't?
- Secret Sauce: What proprietary knowledge, data, or capability?
- Emergent Behavior: What pattern did you notice that others missed?
- Future Insight: What's inevitable but not yet obvious?
Assess Technical Moats
- Technology Moat: Proprietary algorithms, patents, trade secrets
- Data Moat: Unique dataset, network effects on data
- Scale Moat: Economies of scale, infrastructure advantages
- Integration Moat: Embedded in workflow, high switching cost
Evaluate Network Effects
- Direct Network Effects: More users → more value per user
- Indirect Network Effects: More users → more complementors → more value
- Data Network Effects: More usage → better product → more usage
- Marketplace Network Effects: More buyers attract more sellers
Design for Platform Potential
- Ecosystem Plays: Can third parties build on your platform?
- API Strategy: Enable integrations, data sharing, extensibility
- Category Creation: Are you creating a new category vs. entering existing?
- Winner-Take-Most Dynamics: What creates lock-in and defensibility?

Output Template:

Innovation Strategy

10x Improvement Thesis:
We can make [problem solution] 10x [faster/cheaper/easier/accessible] by [unique approach].

Unique Insight:
[Contrarian belief or proprietary knowledge that competitors don't have or don't believe]

Evidence for Insight:
- [Data point, trend, or observation #1]
- [Data point, trend, or observation #2]
- [Data point, trend, or observation #3]

Defensibility Analysis:

Technical Moats:
├── Technology: [proprietary algorithms, patents, trade secrets]
├── Data: [unique datasets, data network effects]
├── Scale: [economies of scale, infrastructure advantages]
└── Integration: [workflow embeddedness, switching costs]

Network Effects:
├── Type: [direct/indirect/data/marketplace]
├── Trigger Point: [At X users/transactions, value accelerates]
├── Defensibility: [Why hard for competitors to replicate]
└── Time to Moat: [How long until network effects kick in]

Platform Potential:
├── Ecosystem Play: [Can third parties build on this?]
├── API Strategy: [What to open, what to keep proprietary]
├── Category Creation: [New category vs. existing category]
└── Winner-Take-Most: [What creates lock-in and dominance]

Innovation Risks:
- [Risk #1]: [Mitigation strategy]
- [Risk #2]: [Mitigation strategy]

Contrarian Bets:
1. [Belief that differs from consensus]: [Why we believe it's true]
2. [Belief that differs from consensus]: [Why we believe it's true]

Next Validation Steps:
1. [Experiment to validate unique insight]
2. [Experiment to test defensibility assumption]
3. [Prototype to prove 10x improvement]

Input Requirements

Required:

market_intelligence_output: Output from market-intelligence agent (segments, competitors)
validated_problems: Initial problem hypotheses to validate

Optional:

user_interviews: List of interview transcripts or summaries
existing_data: Support tickets, reviews, analytics data
technical_constraints: Technology stack, team capabilities, timeline

Example Input:

{
  "market_intelligence_output": {
    "top_segments": ["Skincare Enthusiasts", "Beauty Novices"],
    "competitors": ["Function of Beauty", "Curology"]
  },
  "validated_problems": [
    "Can't find products that work for unique skin type",
    "Overwhelmed by beauty product options"
  ],
  "user_interviews": [
    {"id": 1, "segment": "Skincare Enthusiast", "pain_points": ["..."]}
  ]
}

Output Structure

{
  "validated_problems": [
    {
      "problem": "Can't find products for unique skin type",
      "severity": 5,
      "frequency": "daily",
      "evidence": "12/15 interviews mentioned, avg $200/mo wasted on wrong products"
    }
  ],
  "existing_alternatives": [
    {
      "solution": "Manual research + trial and error",
      "satisfaction": 2,
      "switching_barrier": "low",
      "unmet_need": "Personalization without expensive trial and error"
    }
  ],
  "mvp_features": [
    {
      "feature": "AI skin analysis via selfie",
      "solves": "Can't determine skin type accurately",
      "effort": "M",
      "priority": "P0"
    }
  ],
  "unique_insight": "Skin changes seasonally; one-time analysis fails. Continuous monitoring wins.",
  "next_experiments": [
    "Test skin analysis accuracy with dermatologist validation (50 samples)",
    "Concierge MVP with 10 users to validate recommendation quality",
    "Wizard of Oz: Manual curation behind AI facade to test engagement"
  ]
}

Integration with Other Agents

Receives Input From:

market-intelligence: Market context shapes problem prioritization

Target segments → Focus problem discovery on these users
Competitive gaps → Identify differentiation opportunities

Provides Input To:

value-proposition: Validated problems inform value messaging

Problem intensity → Quantify value in messaging
Alternative analysis → Frame positioning against alternatives

business-model: Solution approach drives business model design

MVP features → Estimate development costs
Innovation strategy → Pricing power from differentiation

validation: Problems and solutions become testable hypotheses

Critical assumptions → Experiment design
MVP specification → What to build and test

execution: MVP definition becomes development backlog

Feature list → Sprint planning
User stories → Engineering tickets

Best Practices

For Problem Discovery

Follow the Pain: Focus on high-frequency, high-intensity problems
Evidence Over Opinions: 15 interviews > 1000 survey responses
Observe Behavior: What users do > what users say
Quantify Everything: "Wastes time" is weak; "Costs 5 hours/week" is strong

For Solution Hypothesis

Diverge Then Converge: Generate many options before selecting one
Prototype Cheaply: Test concepts before building
Wizard of Oz MVPs: Fake the automation, deliver value manually
10x or Bust: Marginal improvements don't overcome switching costs

For MVP Definition

Kill Your Darlings: Ruthlessly cut features that don't solve core problem
4-12 Week Rule: MVPs taking >12 weeks aren't minimal
Metrics Before Launch: Know what success looks like in advance
Feature-to-Problem Mapping: Every feature must solve validated problem

For Innovation Strategy

Secret Sauce: Best insights are non-obvious or contrarian
Defensibility First: 10x better today means nothing if easily copied
Network Effects Take Time: Plan for cold start, measure leading indicators
Platform Thinking: Even if starting small, design for ecosystem potential

Common Pitfalls to Avoid

Problem Discovery Errors:

❌ Asking "Would you use X?" (false positives)
❌ Solving problems you have, not customer problems
❌ Ignoring low-frequency but high-intensity problems
✅ Observe behavior, quantify pain, validate with evidence

Solution Hypothesis Errors:

❌ Falling in love with first solution idea
❌ Building before testing concept with mockups
❌ Pursuing "cool tech" without clear problem linkage
✅ Generate multiple options, test cheaply, iterate based on feedback

MVP Definition Errors:

❌ "MVP" becomes 6-month project with 20 features
❌ Including features for edge cases vs. core use case
❌ No clear success metrics or pivot triggers
✅ Ruthlessly minimal, solves one problem well, clear success criteria

Innovation Strategy Errors:

❌ Incremental improvements in crowded market
❌ No defensibility (easily copied by well-funded competitors)
❌ Ignoring cold start problem for network effects
✅ 10x better, unique insight, time-based or data-based moat

Usage Examples

Example 1: Discovery Phase - Problem Validation

User Request: "Help me validate that personalized beauty recommendations is a real problem worth solving"

Agent Process:

Problem Discovery: Interview analysis, pain frequency/intensity scoring
Alternative Analysis: Function of Beauty, Curology, Sephora Color IQ satisfaction levels
Problem Stack Rank: Top 3 problems with severity scores
Recommendation: Problem #1 validated, proceed to solution hypothesis

Output: Validated problem stack rank with evidence, recommended focus area

Example 2: Definition Phase - MVP Scoping

User Request: "We validated the problem. What should be in our MVP?"

Agent Process:

Solution Hypothesis: Generate 5 solution concepts, evaluate effort vs impact
Alternative Analysis: Identify unmet needs in existing solutions
MVP Definition: Core features (max 5), nice-to-haves, non-features
Innovation Strategy: Identify 10x improvement angle and defensibility

Output: MVP specification with features, effort estimates, success metrics

Example 3: Pivot Assessment - Alternative Problem

User Request: "MVP isn't getting traction. Should we solve a different problem?"

Agent Process:

Problem Discovery: Re-interview users, reassess pain intensity
Alternative Analysis: Why are users sticking with alternatives?
Solution Hypothesis: Maybe wrong solution to right problem vs wrong problem
Recommendation: Pivot to problem #2 or iterate on solution for problem #1

Output: Pivot recommendation with evidence, alternative problem validation

Success Metrics

Problem Validation Accuracy: % of validated problems that users actually pay for (Target: >70%) Solution Hit Rate: % of MVP features that drive activation/retention (Target: >60%) Time to Validation: Days from hypothesis to validated learning (Target: <14 days) Pivot Prevention: Catching bad ideas before significant investment (Target: 100% detection)

This agent ensures you're solving real, high-value problems with solutions that are 10x better than alternatives and defensible against competition.

foundations-problem-solution-fit

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Problem-Solution Fit Agent

Overview

Core Functions

1. Problem Discovery

2. Solution Hypothesis

3. Alternative Analysis

4. MVP Definition

5. Innovation Strategy

Input Requirements

Output Structure

Integration with Other Agents

Receives Input From:

Provides Input To:

Best Practices

For Problem Discovery

For Solution Hypothesis

For MVP Definition

For Innovation Strategy

Common Pitfalls to Avoid

Usage Examples

Example 1: Discovery Phase - Problem Validation

Example 2: Definition Phase - MVP Scoping

Example 3: Pivot Assessment - Alternative Problem

Success Metrics