## Building a Real AI Agent

Learn how to build a real AI agent, not just a chatbot, that can think, act, and adapt across multiple environments. This guide by Will Sentance, Chief AI Officer of Codesmith, breaks down the four-stage agent loop (Input → Reasoning → Action → Output) and shares best practices for logging, error handling, and building resilient multi-runtime systems.

### Overview
Step-by-step walkthrough using Python, OpenAI, Automator, and Google Apps Script. Real example: turning screenshots into automated Google Calendar events. Ideal for developers looking to master AI orchestration, automation, and hands-on workflow design.

### Understanding AI Agents
When people think about AI, they often imagine a single model call: you send a prompt to an LLM, and you get a response back. That’s powerful, but it’s not an agent.

An AI agent is more than an API call. It's a system that can reason, act, and adapt across multiple runtimes. It’s about chaining logic, managing actions, handling errors, and building workflows that can actually execute in the real world. In this article, I’ll break down how to build an AI agent from the ground up, based on the principles I use in practice.

### The Four-Stage Loop
Every agent follows the same four-stage loop:
1. **Input** - the agent receives something: a user query, a goal, or an external event trigger.
2. **Reasoning** - the agent decides what to do. This often involves breaking down a task into smaller steps.
3. **Action** - the agent executes those steps. This could mean calling APIs, searching files, or running scripts.
4. **Output** - the agent provides an answer, triggers automation, or sends data elsewhere. This flow repeats. An agent may reason, act, and observe several times before it decides it has reached a final answer.

### Importance of Logging
When you write plain JavaScript or Python, debugging is simple: console.log or print tells you what’s going on. With an agent, that doesn’t work.

Here’s why: agents often run across multiple runtimes. Some parts execute locally, others in the cloud, others inside a third-party service. Without proper logging, you have no visibility into what’s happening. Centralize your logs instead of scattering them across services.

- **Tag logs** with the runtime or environment where they were generated.
- **Split info logs** (reasoning, step flow) from error logs (failures).
- **Keep logs human-readable;** you’ll need them when debugging multi-step failures.

### Designing for Resilience
Agents will fail. APIs timeout, models hallucinate, and services crash. The goal is not to prevent failure, but to design for resilience:
- **Retries with backoff** - If an API call fails, retry it with exponential delays.
- **Fallbacks** - Have a secondary plan. If one tool fails, try another.
- **Source awareness** - Distinguish between errors caused by your own logic, the model, or an external service.

### Choosing the Right Programming Language
I’m often asked: should you build agents in Python or JavaScript?
- **Python** is the industry standard for AI/ML systems with a rich ecosystem of AI libraries (PyTorch, TensorFlow, scikit-learn) and strong tooling for orchestration frameworks like LangChain and LangGraph.
- **JavaScript** shines for agents that are web-first. If your agent lives in a browser or interacts heavily with frontend logic, JS can be simpler.

### Key Components
To make this practical, here are the components you need to wire up:
- **The LLM** is the reasoning layer. It decides what action to take next. Think of it as the brain, not the whole body.
- Agents need “hands” to interact with the world.
- **Tools** can be: After taking an action, the agent must “see” what happened.

### Integrating Logging
Integrate logging at every step and wrap tool calls with try/except or equivalent. Every action should either return a result or a clear error message.

### Creating the Calendar Event
We’ll pass our structured JSON data to a Google Apps Script endpoint that uses the Google Calendar API to automatically create an event.

### Testing the Full Workflow
Finally, we’ll see all the pieces come together! We will take a screenshot of an event and see a brand-new calendar event appear with everything logged step-by-step.

### Conclusion
An AI agent is not a toy. It’s a distributed system that requires architecture, resilience, and care. Start small: wire up the four-step loop (input → reasoning → action → output). Then add logging, error handling, and a couple of tools. From there, expand into frameworks, private models, and advanced automation.

The key is to remember: the LLM is the brain, not the whole system. The agent comes alive when you connect reasoning, tools, and feedback into a loop that can actually get things done. That’s how you build an AI agent. Not just a chatbot, but a system that thinks, acts, and adapts.

Will Sentance is the co-founder of Codesmith, where he’s driven the mission to equip diverse learners with the skills and mental models to thrive in software and AI.