Winning Diagnosis:
Unclear Agent Scope
Self-serve AI agent users stuck at step 3 need pre-built templates to define agent scope.
Pre-defined templates for narrow agent tasks reduce cognitive load and help users see achievable value quickly, increasing onboarding completion and early engagement.
Mixed — Early signals only-problem definition needs strengthening
- check_circleYou want a structured diagnosis and low-regret remediation path
- warningYou already know the root cause and only need implementation help
READY TO START?
Everything you need to diagnose the issue and implement a real fix.
Root cause diagnosis
→ What is actually causing the issue
Prevention framework
→ How to avoid future issues
Priority order
→ What to fix first and why
Resolution steps
→ Step-by-step fix plan
Why This Won
- check_circleThe solution can be designed and tested within a week by a two-person team, minimizing development risk while maximizing learning speed
- check_circleUser feedback and session recordings show clear evidence of hesitation and uncertainty at step 3, validating that this is a high-impact fix
- •Reasonable path to resolution in ~7 days
- warningProviding hyper-specific templates may limit user flexibility and prevent creative use cases. Users may rely too heavily on templates and fail to build customized agents that fit their unique needs
- warningThe proposed solution may not be implemented quickly or effectively due to lack of prior experience with similar interventions. Without a testable implementation plan, the solution may fail to resolve the drop-off issue and waste resources
- +High drop-off rate at step 3 of onboarding. Indicates a bottleneck in the user journey that is preventing activation and conversion to active users
- +Low engagement with open-ended prompts in step 3. Suggests users are unsure how to define an agent's scope, pointing to a usability or guidance problem
READY TO START?
Everything you need to diagnose the issue and implement a real fix.
Root cause diagnosis
→ What is actually causing the issue
Prevention framework
→ How to avoid future issues
Priority order
→ What to fix first and why
Resolution steps
→ Step-by-step fix plan
- •Reasonable path to resolution in ~7 days
- warningProviding hyper-specific templates may limit user flexibility and prevent creative use cases. Users may rely too heavily on templates and fail to build customized agents that fit their unique needs
- warningThe proposed solution may not be implemented quickly or effectively due to lack of prior experience with similar interventions. Without a testable implementation plan, the solution may fail to resolve the drop-off issue and waste resources
- +High drop-off rate at step 3 of onboarding. Indicates a bottleneck in the user journey that is preventing activation and conversion to active users
- +Low engagement with open-ended prompts in step 3. Suggests users are unsure how to define an agent's scope, pointing to a usability or guidance problem
Design and test three hyper-specific agent templates (e.g., 'Order Status Checker', 'FAQ Responder', 'Appointment Reminder') with 10 users to see if setup completion improves.
Other viable diagnosis paths
These didn't win — here's where the winner pulled ahead
Missing Use Case Clarity
Users likely lack clarity on specific, feasible use cases tailored to their niche; introduce use-case examples…
How this played out
The story of the run15 unique diagnosis paths generated across multiple root-cause angles to maximize coverage.
Top diagnoses were tested against root-cause strength, remediation clarity, and recurrence prevention.
13 lower-conviction diagnosis paths dropped as signals showed weaker evidence or less reliable remediation.
Unclear Agent Scope separated on diagnosis strength, fix clarity, and execution confidence.
Technical competition logsView the final arena state and phase-by-phase outcomesexpand_more
Archived technical view of the completed run.
- •7d to resolve — medium execution risk
- •The high drop-off at step 3 is most likely due to users struggling to define a…
- •Confidence: Medium–High
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- •Holding up under critique
- •The claim that the solution can be implemented within a week by a two-person team lacks...
- •The prevention framework relies on continuous monitoring and iteration but does not clearly...
- •Still true — The root cause diagnosis is well-supported by user behavior data and feedback, showing…
- •Confidence medium — weak evidence support
- •Diagnosis risk: medium · medium execution
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- •Holding up under critique
- •The 60% return rate to step 1 is cited without source attribution, weakening the credibility of...
- •The prevention framework relies heavily on continuous feedback collection without addressing...
- •Still true — The root cause is clearly tied to a lack of guidance in defining use cases, supported…
- •Confidence medium — weak evidence support
- •Diagnosis risk: medium · low execution
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●Unclear Agent Scope
Users likely struggle to define a sufficiently narrow and valuable agent scope; provide pre-defined, hyper-specific…
- •Finished #1 with final score 65
- •The 'Unclear Agent Scope' candidate has a slightly higher initial score but suffers from weaker evidence quality and unverified claims about implementation speed and user behavior. The solution, while plausible, lacks the specificity and testability needed for a small team to execute quickly. The unsupported claims and lack of concrete evidence reduce its viability and trustworthiness.
- •Diagnosis risk ended medium
- •Verification confidence was medium
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●Missing Use Case Clarity
Users likely lack clarity on specific, feasible use cases tailored to their niche; introduce use-case examples…
- •Finished #2 with final score 64
- •The 'Missing Use Case Clarity' candidate offers a more coherent and testable solution that aligns with the operator's capabilities and target audience. It provides a clearer path to execution by focusing on segmented use-case examples, which is more actionable for a two-person team. The evidence is more specific and realistic, and the assumptions are framed with greater honesty, making it more viable for rapid implementation and validation.
- •Diagnosis risk ended medium
- •Verification confidence was medium
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Decisive Analysis
Eliminated diagnosis path
System Provenance
AI-generated solution, stress-tested for effectiveness. May contain assumptions, inaccuracies, or incomplete context. Verify before applying.