AI Agents Projects
Build intelligent AI agents that reason, plan, and use tools to accomplish complex tasks
AI Agents Projects
Build autonomous AI systems that think, plan, and act
AI Agents Projects
Create intelligent agents that go beyond simple question-answering to accomplish complex tasks through reasoning and tool use.
Learning Path
┌─────────────────────────────────────────────────────────────────────────────┐
│ AI AGENTS LEARNING PATH │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ BASIC │ │
│ │ Tool Calling Agent │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ INTERMEDIATE │ │
│ │ │ │
│ │ ReAct Agent ──► Conversational ──► Planning Agent ──► MCP │ │
│ │ │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ADVANCED │ │
│ │ │ │
│ │ Multi-Agent ──► Autonomous ──► Voice Agent ──► Evaluation │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ Bull vs Bear ──► Diagnosis ──► Drug Interaction ──► Tumor Board │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Tool Calling Agent | Build an agent that uses functions and APIs | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| ReAct Agent | Implement reasoning and acting patterns | ~4 hours |
| Conversational Agent | Add memory and context to your agents | ~4 hours |
| Planning Agent | Build agents that plan before executing | ~5 hours |
| MCP Tool Integration | Build agents with Model Context Protocol for standardized tool integration | ~6 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Multi-Agent System | Orchestrate multiple specialized agents | ~3 days |
| Autonomous Agent | Build self-directed agents with goal decomposition | ~4 days |
| Production Voice Agent Platform | Real-time ASR + TTS + LLM voice system with monitoring and evaluation | ~4-6 days |
| Agent Security & Safe Deployment | Threat modeling, guardrails, and secure tool access | ~3 days |
| Agent Evaluation | Test and evaluate agent performance | ~2 days |
| Bull vs Bear Market Analyst | Adversarial debate for investment analysis with Bull/Bear personas | ~3-4 days |
| Differential Diagnosis Debate | Two physicians debate competing diagnoses to reduce diagnostic errors | ~3-4 days |
| Drug Interaction Arbitrator | Adversarial debate for nuanced drug interaction decisions to reduce alert fatigue | ~2-3 days |
| Tumor Board Simulator | Multi-specialist debate simulating oncology tumor board with 4 AI specialists | ~4-5 days |
Why Learn AI Agents?
| Benefit | Description |
|---|---|
| Automation | Agents handle complex multi-step tasks autonomously |
| Reasoning | LLMs with tools can solve problems humans define |
| Scalability | Deploy agents that work 24/7 without human intervention |
| Flexibility | Agents adapt to new situations using reasoning |
Case Studies
Real-world implementations showing AI agents in production environments.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| AI Research Assistant | Consulting | Autonomous agent for market research and report generation | Available |
| Clinical Decision Support | Healthcare | Multi-step clinical reasoning with drug interactions and safety guardrails | Available |
| Adverse Event Surveillance | Healthcare | Multi-agent system monitoring patient data for adverse drug reactions with supervisor-worker orchestration | Available |
Key Concepts
┌─────────────────────────────────────────────────────────────────────────────┐
│ AI AGENTS KEY CONCEPTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────┐ │
│ │ AI Agents │ │
│ └─────┬─────┘ │
│ ┌──────────┬────────────────┼────────────────┬──────────┐ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ ┌────────┐ ┌────────┐ ┌──────────┐ ┌─────────┐ ┌───────────┐ │
│ │Tool Use│ │Reasoning│ │ Memory │ │Planning │ │Multi-Agent│ │
│ └───┬────┘ └───┬────┘ └────┬─────┘ └────┬────┘ └─────┬─────┘ │
│ │ │ │ │ │ │
│ • Function • ReAct • Short-term • Goal • Orchestration │
│ Calling Pattern • Long-term Decomposition • Communication │
│ • API • Chain of • Episodic • Task • Specialization │
│ Integration Thought Scheduling │
│ • Code • Self- • Error │
│ Execution Reflection Recovery │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Frequently Asked Questions
What is an AI agent and how is it different from a chatbot?
An AI agent is an LLM-powered system that can take actions autonomously to accomplish goals. Unlike chatbots that just respond to queries, agents can use tools (APIs, databases, code execution), make decisions, and execute multi-step workflows. Agents reason about what actions to take, observe results, and adapt their approach.
What is the ReAct pattern for AI agents?
ReAct (Reasoning + Acting) is a prompting pattern where the agent alternates between thinking (reasoning about what to do) and acting (calling tools). The pattern is: Thought → Action → Observation → Thought → ... This creates a reasoning trace that improves reliability and debuggability compared to direct tool calling.
How do I give an agent access to tools?
Use function calling (OpenAI, Anthropic) or tool use APIs. Define tools as JSON schemas describing the function name, parameters, and descriptions. The LLM decides which tool to call based on the user query. Your code executes the actual function and returns results to the LLM for the next step.
What is MCP (Model Context Protocol)?
MCP is an open standard by Anthropic for connecting AI agents to external tools and data sources. Instead of custom integrations for each tool, MCP provides a unified protocol. This allows agents to use a growing ecosystem of MCP servers for databases, APIs, file systems, and more.
How do I prevent agents from making mistakes or going off-track?
Key strategies include: (1) Clear system prompts with explicit boundaries, (2) Tool parameter validation, (3) Human-in-the-loop for critical actions, (4) Iteration limits to prevent infinite loops, (5) Confidence thresholds for escalation, (6) Comprehensive logging and observability.
When should I use a single agent vs multi-agent system?
Use a single agent for focused tasks with clear boundaries. Use multi-agent systems when you need: specialized expertise (research agent + writing agent), parallel processing, complex workflows with handoffs, or separation of concerns. Multi-agent adds complexity, so start simple and add agents only when needed.
Start with the Tool Calling Agent project to learn the fundamentals.