PromptKey – Become an AI Engineer by Building Real Projects

Nebula Corp's support agent answers questions correctly but forgets everything between turns. A user asks 'What plan is Alice on?' and gets the right answer, but when they follow up with 'How much does that plan cost?' the agent has no idea what 'that plan' refers to. Implement a conversation memory system that stores past exchanges so the agent can resolve references and maintain context across turns.

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advanced🎯 45–65 minmissionRank 09PRO

M-045Build a Multi-Agent Code Review System

Nebula Corp wants to automate code reviews using multiple specialized agents. The system should have three agents: a Security Reviewer that checks for vulnerabilities, a Style Reviewer that checks coding standards, and a Coordinator that collects reviews and produces a final summary. The agent definitions exist but the coordination logic is missing — wire up the multi-agent pipeline so the coordinator delegates to reviewers and aggregates their findings.

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advanced🎯 40–55 minmissionRank 07PRO

M-046Build a RAG-Powered Knowledge Agent

Nebula Corp wants an intelligent knowledge assistant that combines RAG retrieval with agent capabilities. The agent should: receive a user question, search a document collection for relevant context, and if the retrieved context is insufficient, reformulate the query and search again. This is the 'agentic RAG' pattern — the agent decides when retrieval is good enough and when to retry. The retrieval and LLM pieces exist, but the agent loop that ties them together is missing.

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intermediate🎯 35–55 minmissionRank 05PRO

M-042Build a ReAct Web Research Agent

Nebula Corp wants a research agent that can answer complex questions by searching the web, reading pages, and synthesizing information. The agent should follow the ReAct pattern: Think about what to do, Act by calling a tool, Observe the result, and repeat until it has enough information to answer. The skeleton has tools for searching and reading, but the ReAct loop isn't implemented yet.

beginner🎯 15–30 minmissionRank 05

M-039Build Your First Agent Router

Nebula Corp's support system needs an agent that can decide which tool to use based on the user's message. Build a simple intent router that analyzes the user message, picks the right tool from a registry, and returns the tool's response. The router should match keywords to tools and handle cases where no tool matches.

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beginner🎯 20–35 minmissionRank 05PRO

M-038Build Your First Tool-Calling Agent

Nebula Corp needs a simple agent that can look up customer information using tools. The skeleton is there — a tool registry and an LLM loop — but the agent doesn't actually call any tools yet. Wire up the tool execution so the agent can receive a tool call from the LLM, execute it, and feed the result back into the conversation.

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intermediate🎯 25–40 minmissionRank 05PRO

M-043Debug the Broken Tool-Calling Loop

Nebula Corp's AI agent is stuck in an infinite loop. The agent is supposed to call a weather tool, parse the response, and return a final answer to the user — but it never stops looping. The tool gets called over and over, and the agent never produces a result. Find the bug in the tool-response parsing logic and fix the loop so the agent terminates correctly.

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intermediate🎯 30–45 minmissionRank 07PRO

M-040Implement Graceful Error Recovery for Agent Tools

Nebula Corp's agent crashes whenever a tool call fails — a network timeout, an invalid argument, or a missing API key brings the whole agent down. Instead of crashing, the agent should catch tool errors, inform the LLM what went wrong, and let it retry with corrected arguments or choose an alternative approach. Implement error handling that makes the agent resilient.

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advanced🎯 40–55 minmissionRank 07PRO

M-044Optimize the Multi-Step Agent Plan

Nebula Corp's AI agent planner is generating bloated, inefficient plans for user requests. A simple task like 'Book a flight and hotel for next Friday' produces 12 steps when 5 would do — redundant lookups, unnecessary confirmations, and repeated tool calls are burning through tokens and latency. Refactor the planning function to produce leaner plans that eliminate redundant steps while still completing every required action.

🔧Workshops

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advanced🔧 55–75 minworkshopRank 07PRO

W-014Agent Task Planner

Create an agent that breaks down complex tasks into subtasks using tree-of-thoughts planning.

planningtree-of-thoughtstask-decomposition

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intermediate🔧 50–75 minworkshopRank 05PRO

W-013Function Calling Wrapper

Build a universal function calling wrapper that works across OpenAI, Anthropic, and local models.

function-callingapi-abstractionerror-handling

🚀Projects

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advanced🚀 660–890 minprojectRank 09PRO

P-005Multi-Agent Research Assistant

Create a supervisor-worker multi-agent system that can research topics, synthesize information, and generate reports autonomously.

ai-agentsorchestrationtool-callingmulti-agentdeployment