Choose Your Level
Pick the difficulty that matches where you are. You can come back and try a harder level later.
Design an Agent Handoff
Write the rules for when a basic chatbot should escalate to a specialized AI.
Map a Researcher-Writer Flow
Design a two-agent system where one AI researches and the other writes.
Build a Self-Correcting Agent Loop
Design an autonomous coding loop with a Generator Agent and a Critic Agent.
AI Agent Flow & Tool-Calling Architecture (Function Calling)
AI agents execute autonomous multi-step tasks by calling external tools and APIs. AI engineers build agent workflows using the ReAct framework (Reason + Act), OpenAI Function Calling / Gemini Tool Use, JSON tool schema definitions, and loop control guards.
1. AI Agent ReAct Loop Architecture Diagram
Flowchart illustrating Thought -> Action -> Observation -> Thought execution loops for multi-tool AI agents.
2. Tool Calling JSON Schema Specification
Collection of tool JSON schemas defining function names, parameters, descriptions, and required arguments.
3. Multi-Step Agent Execution Trace Log
Trace log detailing agent tool calls, API request-response payloads, state updates, and goal completion.
Frequently Asked Questions (Agent Flow Design)
How does the ReAct (Reason + Act) agent loop work?
The agent generates a Thought about the current state, selects an Action (tool call), receives an Observation (tool execution output), and repeats until reaching the final answer.
Why are clear tool descriptions critical for OpenAI Function Calling?
The LLM relies entirely on tool description text to determine WHEN and HOW to invoke a tool; vague descriptions lead to misfires or incorrect argument formatting.
How do you prevent AI agents from getting stuck in infinite execution loops?
Implement maximum step limits (e.g. max 5 iterations), timeout guards, duplicate tool call detection, and explicit human-in-the-loop fallback triggers.
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