Building Your First AI Agent: Tools, Loops, and Orchestration

An AI agent is an LLM that can take actions, not just generate text. Instead of producing a single response, an agent runs in a loop: it thinks about what to do, calls a tool, observes the result, and decides what to do next.

This is the difference between a chatbot and a system that can actually get things done.

What Makes an Agent Different

A standard LLM call looks like this:

Input: "What's the weather in Tokyo?"
Output: "I don't have access to real-time data, but..."

An agent call looks like this:

Input: "What's the weather in Tokyo?"
Think: "I need to check the weather API"
Action: call_weather_api(location="Tokyo")
Observe: {"temp": 22, "condition": "sunny"}
Output: "It's 22C and sunny in Tokyo right now."

The key ingredients are tools (functions the agent can call) and a reasoning loop (the think, act, observe cycle).

The Agent Loop

Every agent follows the same basic loop:

1. Receive the user's request
2. Think about what step to take next
3. Act by calling a tool (or responding to the user)
4. Observe the tool's output
5. Repeat steps 2 to 4 until the task is complete

In code, this looks roughly like:

while True:
    response = llm.generate(messages)

    if response.has_tool_call:
        result = execute_tool(response.tool_call)
        messages.append(tool_result(result))
    else:
        return response.text  # Final answer

The LLM decides when to call tools and when to stop. You define the tools, the LLM handles the orchestration.

Defining Tools

A tool is just a function with a clear description. Here is an example using the standard function-calling format:

{
  "name": "search_docs",
  "description": "Search the knowledge base for relevant documents",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "The search query"
      }
    },
    "required": ["query"]
  }
}

Good tool descriptions are critical. The LLM decides which tool to use based on the description, not the function name. Be specific: "Search the internal knowledge base for HR policy documents" is better than "Search documents."

Common Agent Patterns

ReAct (Reasoning + Acting)

The agent alternates between reasoning steps ("I need to find the user's order first") and action steps (calling a tool). This is the most common pattern and works well for multi-step tasks.

Plan and Execute

The agent creates a full plan upfront ("Step 1: search orders, Step 2: check refund eligibility, Step 3: process refund"), then executes each step. Better for complex workflows where you want visibility into the plan before execution starts.

Multi-Agent

Multiple specialized agents collaborate. One agent handles research, another handles code generation, a third handles review. Each agent has its own tools and system prompt. Frameworks like CrewAI and AutoGen support this pattern.

What Can Go Wrong

Infinite loops: The agent keeps calling tools without making progress. Always set a maximum iteration limit (5 to 10 is typical).

Wrong tool selection: The agent picks the wrong tool because descriptions are ambiguous. Test your tool descriptions with edge cases.

Hallucinated parameters: The agent invents parameter values instead of extracting them from context. Use strict schemas and validate inputs before executing.

Cost explosion: Each loop iteration is an LLM call. A 10-step agent workflow with GPT-4 level models can cost $0.50 or more per request. Use smaller models for simple routing decisions.

Build One Now

Reading about agents only gets you so far. The real learning happens when you define tools, write orchestration logic, and debug why your agent chose the wrong path.

ByteMentor's Agent Builder gives you a hands-on environment to define tools, write agent instructions, and test against real scenarios. You get scored on task completion and efficiency.

If you are new to agents, start by building a simple two-tool agent (search + answer), then add complexity. The AI Agents learning track covers the theory, and the Agent Builder lab is where you practice.

Key Takeaways

• Agents are LLMs that run in a loop: think, act, observe, repeat
• Tools are functions with clear descriptions that the LLM calls autonomously
• Start simple (2 to 3 tools) and add complexity gradually
• Always set iteration limits and validate tool parameters
• The best way to learn is to build one