AI & Data
AI Agent Orchestration Challenges
AI Agent Orchestration challenges put you inside the work of building AI agents that reason, act, and use tools reliably. You'll develop skills in function calling, tool use, and the ReAct pattern, moving from single-agent workflows to systems with agent memory that hold context across steps.
From there you'll take on the harder edges — multi-agent orchestration, multi-modal agents, and the Model Context Protocol — wiring LangGraph or CrewAI workflows and instrumenting agent observability and agent evaluation the way production teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- CodeIntermediateNew
Parse and Structure Clinical Discharge Summaries
Combine traditional NLP (section segmentation, sentence parsing) with LLM extraction (small open model + structured-output enforcement). Build the pipeline so every extracted fi…
- Structured Extraction
- Clinical NLP
- Parsing
Natural Language Processing - AnalysisIntermediateNew
Evaluate an Agent Suite on the SWE-Bench-Style Coding Benchmark
You receive a sandboxed set of 50 small repo-modification tasks (test-passing as the success signal). Run 3 open-source agent frameworks (e.g., OpenHands, SWE-agent, and Aider) …
- Ai Agents
- Agent Evaluation
- Benchmarking
AI Agents and LLM-Based Agents - CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG Architectures
- Cross Lingual Retrieval
- Multilingual Embeddings
Neural Networks for NLP - CodeIntermediateNew
Multi-Agent Research Assistant for Biotech Patent Review
You receive 20 historical patent applications with the firm's own prior-art memos as ground truth. Design and build a 3-agent system: (a) Searcher — issues queries to a patent-s…
- Ai Agents
- Multi Agent Collaboration
- Agent Evaluation
AI Agents and LLM-Based Agents Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Build an Internal-Tools Agent for a Mid-Cap Enterprise
You receive OpenAPI specs for 4 mock internal APIs and 30 reference question-answer pairs spanning easy lookups and multi-tool chains. Build the agent using an LLM tool-use fram…
- Ai Agents
- Tool Use
- Agent Evaluation
AI Agents and LLM-Based Agents - CodeIntermediateNew
Agentic RAG with Context-Window Budgeting
You receive a synthetic dataset of 60 founder-style queries paired with 'workspaces' (each up to 500 documents across 3 source types), plus gold-standard answers and citation li…
- Agentic RAG
- Context Window Management
- Iterative Retrieval
Retrieval-Augmented Generation - CodeIntermediateNew
ReAct Agent for Legal-Research Tool-Use
You receive 30 research questions with paralegal-written gold answers and citation lists, plus stubbed implementations of the 4 tools (you do not need to build retrieval — just …
- React Prompting
- Tool Use
- Agent Design
Prompt Engineering - CodeIntermediateNew
Multi-Turn Dialogue Manager for a Banking Assistant
You receive a transcript dataset of 200 conversations (human-tagged with intent, slot values, and required outcome), a list of 8 supported intents, and tool stubs for 3 backend …
- Dialogue Management
- Intent Classification
- Slot Filling
Question Answering and Conversational Systems - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- StrategyBeginnerNew
Plan a Self-Improving Sales-Research Agent
Build the v0 agent: given a company URL, it gathers 5 fact bullets (recent news, headcount range, tech stack hints, hiring patterns, a recent leadership change) and drafts a 4-l…
- Ai Agents
- Agent Design
- A/B Testing & Experimentation
AI Agents and LLM-Based Agents - DesignBeginnerNew
Conversational UI for a Personal-Finance Assistant
You will work from 4 scripted scenarios: 'how much did I spend on coffee last month', 'why did my rent payment fail', 'help me set up an emergency fund', and an out-of-scope 'is…
- Conversational Ui
- Dialogue Design
- Trust Design
Question Answering and Conversational Systems - CodeIntermediateNew
Build a Tool-Calling Agent for an Internal Reporting Bot
You will implement the agent in either LangChain or LlamaIndex (your choice; defend it in the readme). Wire 4 tools: (1) read-only SQL on a sample warehouse, (2) a mocked BI met…
- Agent Orchestration
- Tool Calling
- Langchain Or Llamaindex
LLM Application Development - CodeBeginnerNew
Build a Math Intelligent-Tutoring Assistant for High Schoolers
You receive: a curated set of 40 algebra problems with worked solutions, the company's pedagogy rubric ('hint, don't reveal' principle), and a baseline 'just answer' chatbot for…
- Intelligent Tutoring
- Prompt Patterns
- Ai Agents
AI in Education and Learning Analytics Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Build a LangGraph Multi-Agent Researcher
Design the four-agent topology with explicit message contracts. Implement each agent as a separate LLM call with role-specific system prompts, tool access (web search for resear…
- Multi Agent Orchestration
- Langgraph Or Crewai Workflows
- Tool Use
Multi-Agent Systems - ResearchIntermediateNew
Safety-Test a Customer-Service Agent for Adversarial Prompts
You receive a sandboxed instance of the agent (a tool-using LLM that can read account balances and open support tickets — both mocked). Design a red-team suite of at least 80 pr…
- Ai Agents
- Red Team Operations
- Adversarial Prompts
AI Agents and LLM-Based Agents
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.