01 · Challenge
Career planning combines research, skill-gap assessment, sequencing, and synthesis, which can become unfocused when handled by one broad prompt.
Multi-agent planning · Career intelligence
A multi-agent career-planning notebook that delegates research, skill-gap analysis, sequencing, and roadmap synthesis to clearly scoped CrewAI roles.

Case study brief
01 · Challenge
Career planning combines research, skill-gap assessment, sequencing, and synthesis, which can become unfocused when handled by one broad prompt.
02 · Response
I used CrewAI to divide the problem among specialist agents and combine their outputs into a practical, sequenced roadmap.
System anatomy
The architecture is expressed as responsibilities and boundaries so the model, workflow logic, interfaces, and human controls remain inspectable.
Specialist agents own research, gap analysis, learning paths, and final synthesis
Explicit tasks and expected outputs keep delegation inspectable
The crew combines findings into a sequenced, actionable roadmap
Implementation
Assigned separate roles for research, gap analysis, learning paths, and final synthesis.
Defined explicit tasks, context, and expected outputs for each agent.
Structured the execution order so later tasks build on reviewed earlier work.
Combined findings into an actionable roadmap rather than isolated suggestions.
Evidence & validation
Executed CrewAI notebook.
Saved final-roadmap evidence and architecture explanation.
Project poster set and LinkedIn publication.
Reflection
Multi-agent collaboration becomes useful when roles reduce ambiguity and outputs have clear contracts; adding agents without boundaries only increases noise.
Next iteration
Outcomes
Clear agent roles and task boundaries
Notebook execution evidence and documented final output
Professional poster and separate project publication