Overview
What this challenge is about.
Build an Internal-Tools Agent for a Mid-Cap Enterprise. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blo...
The Brief
What you'll do, and what you'll demonstrate.
Ship a tool-using LLM agent that resolves 80%+ of reference questions safely, with budgets and guardrails the customer's CIO can sign off on.
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 tool-using LLM agents with explicit budgets and guardrails
- Build an evaluation harness for agent task success
- Reason about failure modes specific to tool-use agents
- Communicate agent architecture to a non-engineering executive
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.
- Tool Use
Apply tool use 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.
- Guardrails
Apply guardrails 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
Building a tool-using agent for an enterprise customer end-to-end is the canonical AI-engineer project at agent-platform startups.
This challenge sharpens
- llm-agents
- tool-use
- guardrails