Where to Start When Building AI Agents
Confused by the hype around AI agents? Here is the step-by-step roadmap to building your first agentic system.
The Agentic Confusion
Everyone is talking about “agents” that can book flights or write code, but where do you actually begin? It is easy to get lost in complex frameworks before understanding the core mechanism.
The Surface View
To the user, an agent looks like a chatbot that can “do things.” You ask a question, and instead of just replying with text, the system performs an action—like searching the web, querying a database, or sending an email—without you needing to intervene.
Under the Hood: The Agent Loop
An agent is essentially a loop that connects three distinct components:
- The Brain (LLM): The model that decides what to do next based on your prompt.
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The Tools: Functions or APIs the model is allowed to call (e.g.,
get_weather(),query_sql()). - The State: A memory buffer that keeps track of previous steps, tool outputs, and the current goal.
The logic follows a cycle: The LLM receives a prompt, decides if it needs a tool to answer, executes that tool, observes the output, and repeats until the task is complete.
Tracing a Simple Task
Imagine you ask an agent: “What is the stock price of Apple?”
- Planning: The LLM receives the prompt and realizes it doesn’t know real-time data.
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Tool Selection: It outputs a command:
call_tool("search_finance", "AAPL"). - Execution: Your system runs the function, which fetches data from an API.
- Observation: The result (e.g., “$175”) is fed back into the LLM.
- Response: The LLM synthesizes the final answer: “The current price of Apple is $175.”
Why It Feels Like Magic
It feels intelligent because the agent doesn’t just predict the next word; it predicts the next action. The trade-off is complexity: you have to handle errors (what if the tool fails?) and prevent the agent from entering infinite loops. Reliability comes from strict input/output schemas for your tools.
Where to Start
Don’t start with complex multi-agent orchestration. Start by building a single-agent loop that can call one local function. Once you master the feedback loop between the LLM and the tool output, you have the foundation for everything else.
