AI Agents Explained: How They Work and How to Build Your First One

Building AI Agents: What They Are and How to Create Them

AI agents are getting a lot of attention right now, but most explanations stay high level and skip what it actually takes to build one.

At a practical level, an AI agent is not just an LLM with a prompt. It is a system that can take a goal, decide what to do next, call tools, observe results, and repeat until it reaches an outcome. The LLM is only the reasoning layer. The real system is everything around it.

What is an AI Agent?

A simple way to think about an agent is as a loop:

1. Take a goal or user input
2. Decide the next action
3. Call a tool or API
4. Observe the result
5. Repeat until done

This loop is what makes agents different from a single model call. Instead of answering once, they can plan and act over multiple steps.

Core Components

To build a useful agent, you need a few key pieces.

1. LLM (Reasoning Layer)

The LLM decides what to do next. It interprets the goal, selects tools, and generates actions.

2. Tools (Execution Layer)

Tools define what the agent can actually do. These can include:

• APIs
• database queries
• external services
• internal microservices

Without tools, the agent cannot take meaningful actions.

3. Control Flow

You need a structure for how the agent operates. This can be:

• simple step-by-step reasoning
• loop-based execution
• graph-based workflows for complex systems

This layer controls how decisions are made and when the agent stops.

4. Memory

For multi-step tasks, the agent needs context. Memory can include:

• conversation history
• intermediate results
• task state

Without memory, agents lose track of progress and become unreliable.

How to Create an AI Agent

A basic approach to building an agent looks like this:

1. Define the goal
2. Define available tools
3. Create a reasoning loop
4. Execute actions and collect results
5. Repeat until completion

Here is a simple pseudo-flow:

In practice, frameworks like LangChain, LangGraph, or Bedrock Agents help structure this loop, but the core idea remains the same.

Where Agents Work Well

Agents are useful when:

• tasks involve multiple steps
• decisions depend on intermediate results
• multiple systems need to be coordinated

Examples include:

• data aggregation from multiple sources
• workflow automation
• multi-step decision systems

Common Mistakes

One common mistake is trying to make the agent do everything internally.

For example:

• running heavy ML models inside the agent
• handling complex computation in the reasoning loop

This usually leads to:

• higher latency
• increased cost
• harder debugging

A better approach is to treat the agent as an orchestrator and let specialized systems handle actual computation.

Final Thoughts

AI agents are not just about prompts. They are about system design.

The shift happening now is from single model calls to systems that can plan, act, and adapt over time. The teams that get this right focus on building reliable tool layers, clear control flow, and well-defined boundaries.

We are still early, but the patterns are starting to emerge.

The real challenge is not calling an LLM. It is designing the system around it.