Building AI Agents from Scratch

Instead of hiding complexity behind abstractions, we will build an AI agent from first principles, using plain Python, explicit tool definitions, and a transparent control loop. By the end, you will understand exactly how agents work — and why modern agent frameworks are designed the way they are.

This article is written for engineers who want control, debuggability, and safety, not magic.

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1. What “Building an Agent” Actually Means

An AI agent is not a special model or a new type of intelligence. It is a system.

At the lowest level:

Agent = Language Model + Tools + Control Loop

• The language model decides what to do next
• Tools are functions it is allowed to call
• The control loop coordinates reasoning, execution, and feedback

Everything else — planning, memory, autonomy — emerges from this loop.

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2. The Minimal Agent Architecture

Before writing code, fix the mental model.

┌────────────┐
│   User     │
└─────┬──────┘
      ↓
┌────────────┐
│  Messages  │  ← conversation state
└─────┬──────┘
      ↓
┌────────────┐
│    LLM     │  ← decides next action
└─────┬──────┘
      ↓
Is a tool call required?
   ├─ Yes → Execute Tool → Append Result → Loop
   └─ No  → Final Answer → Stop

The agent does not remember anything unless you explicitly store it.

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3. Start With a Small, Explicit World

Agents behave better when their world is simple and explicit.

customers = {
    "C1": {"name": "John", "email": "john@example.com"},
    "C2": {"name": "Jane", "email": "jane@example.com"},
}

orders = {
    "O1": {"customer_id": "C1", "status": "pending"},
    "O2": {"customer_id": "C1", "status": "shipped"},
    "O3": {"customer_id": "C2", "status": "canceled"},
}

This mimics a database without adding infrastructure complexity.

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4. Tools Are the Agent’s Only Hands

An LLM cannot access data or perform actions by itself.
It can only request tool execution.

Each tool should be:

• Small
• Deterministic
• Clearly documented

def get_order(order_id: str) -> dict | None:
    """Fetch an order by ID."""
    return orders.get(order_id)

def cancel_order(order_id: str) -> bool:
    """Cancel an order if it exists."""
    if order_id in orders:
        orders[order_id]["status"] = "canceled"
        return True
    return False

Docstrings and type hints significantly improve tool usage accuracy.

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5. Explicit Tool Permissions (Critical for Safety)

Never allow arbitrary function execution.

Instead, define an allowlist:

TOOLS = {
    "get_order": get_order,
    "cancel_order": cancel_order,
}

The agent can only call what you expose.

This single decision prevents most catastrophic failures.

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6. The Agent Control Loop (The Core)

This loop is the agent.

messages = [{"role": "user", "content": user_input}]

while True:
    response = model.chat(messages)

    if response.tool_call:
        name, args = response.tool_call

        if name not in TOOLS:
            raise ValueError("Unauthorized tool")

        result = TOOLS[name](**args)

        messages.append({
            "role": "tool",
            "content": str(result)
        })
    else:
        messages.append({
            "role": "assistant",
            "content": response.content
        })
        break

Nothing magical is happening — just state, decisions, and feedback.

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7. How Multi-Step Reasoning Emerges

Consider this request:

“Cancel all orders for customer C1.”

The agent naturally decomposes the task:

1. Identify customer C1
2. Retrieve associated orders
3. Cancel each order
4. Confirm completion

This behavior emerges without explicit planning code — purely from the loop.

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8. Memory Is Just State You Manage

Agents do not remember anything automatically.

Memory can be:

• Conversation history
• Summaries
• Databases
• Files

If it’s not stored and re-fed, it doesn’t exist.

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9. Why Build From Scratch Before Using Frameworks

Frameworks are powerful — but opaque.

Building from scratch teaches you:

• Where hallucinations originate
• Why tool calls fail
• How infinite loops happen
• Where safety boundaries must exist

Frameworks make sense after you understand the internals.

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10. Common Failure Modes

• Infinite tool loops
• Over-permissioned tools
• Unbounded context growth
• Silent tool failures
• Non-deterministic side effects

All of these are control problems, not model problems.

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11. Production Hardening Checklist

• Tool allowlists
• Execution timeouts
• Structured outputs
• Logging every step
• Version control before mutations
• Sandboxed execution environments

Agents are software systems — treat them like one.

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Final Thoughts

Building AI agents from scratch is not about avoiding frameworks. It is about earning the right to use them.

Once you understand the loop, tools, and state management, you can reason about any agent system with confidence.

That is real leverage.