Overview
What this challenge is about.
Multi-Agent Research Assistant for Biotech Patent Review. Advanced challenge in code. Writing production code that solves real engineering problems, earn a b...
The Brief
What you'll do, and what you'll demonstrate.
Determine whether a multi-agent system produces better first-pass prior-art memos than a single-agent baseline, on the firm's own historical cases.
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
- Design multi-agent systems with explicit role decomposition
- Benchmark multi-agent vs. single-agent on a real task
- Apply human-rater evaluation to LLM-generated long-form outputs
- Communicate agent risk to a non-technical legal audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI Agents and LLM-Based Agents
Master · Ai Systems
Strong alignment
This challenge maps to AI Agents and LLM-Based Agents 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.
- Llm Agents
Apply llm agents to solve real industry problems and demonstrate production-level capability.
- Multi Agent Collaboration
Apply multi agent collaboration to solve real industry problems and demonstrate production-level capability.
- Agent Evaluation
Apply agent 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.
- Retrieval Augmented Generation
Apply retrieval augmented generation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Designing and shipping a multi-agent system for a real legal customer is the kind of project that lets an AI engineer specialize into vertical AI roles.
This challenge sharpens
- multi-agent-collaboration
- llm-agents
- retrieval-augmented-generation