From Zero to AI Agent: What to Expect From a Hands-On AI Workshop

You've heard the buzzwords — AI agents, agentic systems, autonomous workflows. But what does it actually take to build one? And what happens when you walk into a workshop designed to get you there in a single day?

This guide walks you through what a hands-on AI agent workshop looks like from start to finish.

Morning: Understanding the Architecture

The first half of the day is about building mental models. Before you write a single line of code, you need to understand how AI agents actually work in production.

Agent Architecture Patterns

We cover the three most common patterns:

1. ReAct (Reason + Act) — The agent thinks step-by-step, choosing tools to use at each step
2. Plan-and-Execute — The agent creates a plan upfront, then executes each step
3. Multi-Agent Orchestration — Multiple specialized agents collaborate on complex tasks

LLM Selection & Prompt Engineering

Not all language models are equal. You'll learn:

• How to choose the right model for your use case (cost, latency, capability tradeoffs)
• Prompt engineering patterns that produce reliable, structured output
• How to implement memory systems so your agent maintains context across interactions

Midday: Building Your First Agent

After lunch, the laptops come out. This is where the real learning happens.

Hands-On Exercise: Tool-Using Agent

You build a complete AI agent that can:

• Accept natural language instructions
• Break them down into actionable steps
• Use external tools (APIs, databases, file systems)
• Return structured results

The key insight: the agent doesn't need to be smart — it needs to use tools well. Most of the intelligence comes from good prompt design and well-structured tool interfaces.

RAG Integration

We add a Retrieval-Augmented Generation layer so your agent can answer questions about your own documents and data. This is where participants usually have their "aha" moment — suddenly the agent isn't just generic, it's an expert on your business.

Afternoon: Production Patterns

Building a demo is one thing. Building something production-ready is another. The afternoon covers the gaps between prototype and production.

Testing AI Agents

AI agents are non-deterministic, which makes testing tricky. We cover:

• Deterministic testing with mocked LLM responses
• Evaluation frameworks for agent quality
• Regression testing patterns for prompt changes

Monitoring & Observability

You can't manage what you can't measure. We implement:

• Token usage tracking and cost monitoring
• Agent step tracing for debugging
• Quality metrics and alerting

Deployment

Finally, we deploy the agent to a cloud environment:

• Containerized agent deployment
• API gateway setup for external access
• Rate limiting and security patterns

What You Leave With

By the end of the day, every participant has:

• A working AI agent deployed in a cloud environment
• Production-ready code templates they can build on immediately
• A testing framework for validating agent behavior
• Confidence that they can build AI agents independently

Is Your Team Ready?

If your engineering team has been talking about AI agents but hasn't built one yet, a hands-on workshop is the fastest way to bridge the gap.

Explore our Hands-On AI Agents Workshop →