Overview
What this challenge is about.
Build a multi-agent researcher with eval harness, then compare cost vs single-agent baseline to earn your verifiable certificate.
The scenario
The Amsterdam consultancy (around 40 analysts, paid research for European policy clients) is testing whether a multi-agent prototype can do 70% of an analyst's first-draft work at 20% of the time.
The Brief
What you'll do, and what you'll demonstrate.
Build a four-agent research assistant and quantify when the multi-agent topology beats a single-agent baseline on accuracy, citations, and cost.
Earning criteria — what you'll demonstrate
- Implement a multi-agent topology with role-specific prompts and tools
- Define structured message contracts between agents
- Evaluate multi-agent systems honestly against single-agent baselines
- Make a cost-benefit call on multi-agent architectures
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Multi-Agent Systems
Master · Ai Systems
Strong alignment
This challenge maps to Multi-Agent Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Multi Agent Orchestration
Apply multi agent orchestration to solve real industry problems and demonstrate production-level capability.
- Langgraph
Apply langgraph to solve real industry problems and demonstrate production-level capability.
- Llm Tool Use
Apply llm tool use to solve real industry problems and demonstrate production-level capability.
- Rag
Apply rag to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Prompt Engineering
Apply prompt engineering to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Building multi-agent topologies with LangGraph and shipping them with eval harnesses is the AI-engineer skill set that AI-agent and consulting orgs hire for in 2026.
This challenge sharpens
- multi-agent-orchestration
- langgraph
- llm-tool-use
Applied AI Scientist
Designing fair multi-vs-single-agent evaluations and writing the cost-benefit memo is the applied-AI work that product orgs need before scaling agents.
This challenge sharpens
- evaluation
- multi-agent-orchestration
- prompt-engineering
Prompt Engineer
Crafting role-specific prompts and structured contracts that survive multi-step coordination is core prompt-engineering work at agent-platform companies.
This challenge sharpens
- prompt-engineering
- llm-tool-use
- multi-agent-orchestration