Building Your First AI Agent

AI agents represent a paradigm shift from static Q&A to dynamic, goal-directed systems. Let's build one from scratch.

What Makes an Agent?

An agent is more than a chatbot. It can:

Reason about problems step-by-step
Act by calling external tools
Observe the results of its actions
Iterate until the goal is achieved

This is the ReAct (Reasoning + Acting) paradigm.

The Basic Loop

interface AgentAction {
  tool: string;
  input: Record<string, unknown>;
}
 
interface AgentStep {
  thought: string;
  action?: AgentAction;
  observation?: string;
}
 
async function runAgent(goal: string, tools: Tool[]): Promise<string> {
  const steps: AgentStep[] = [];
  
  while (true) {
    // Generate next thought/action
    const response = await llm.complete({
      prompt: formatPrompt(goal, steps, tools),
    });
    
    const parsed = parseResponse(response);
    
    if (parsed.type === 'final_answer') {
      return parsed.answer;
    }
    
    // Execute the tool
    const observation = await executeTool(
      parsed.action.tool,
      parsed.action.input
    );
    
    steps.push({
      thought: parsed.thought,
      action: parsed.action,
      observation,
    });
  }
}

Tool Definition

Tools are the agent's interface to the world:

interface Tool {
  name: string;
  description: string;
  parameters: JSONSchema;
  execute: (input: unknown) => Promise<string>;
}
 
const searchTool: Tool = {
  name: 'web_search',
  description: 'Search the web for current information',
  parameters: {
    type: 'object',
    properties: {
      query: { type: 'string' }
    }
  },
  execute: async (input) => {
    // Implementation
  }
};

Best Practices

Clear tool descriptions — The LLM relies on these to choose tools
Structured outputs — Use JSON mode for reliable parsing
Error handling — Gracefully handle tool failures
Context management — Summarize long conversations

In the next post, we'll explore memory systems for agents that learn over time.