Overview
What this challenge is about.
Build a LangGraph Multi-Agent Researcher. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockchain-verifi...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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