Introduction to AI Agents for Developers

AI agents are reshaping how developers approach complex tasks. Instead of writing every line of logic by hand, agents can reason, plan, and execute multi-step workflows autonomously.

What Is an AI Agent?

An AI agent is a system that uses a large language model (LLM) as its reasoning engine. Unlike a simple chatbot, an agent can:

• Plan: Break a high-level goal into concrete steps
• Act: Execute those steps using tools (APIs, CLI commands, file operations)
• Observe: Inspect the results of each action
• Iterate: Adjust its plan based on what it learns

Building Your First Agent

Here's a minimal agent loop in TypeScript:

async function agentLoop(goal: string) {
  let plan = await llm.plan(goal);

  for (const step of plan.steps) {
    const result = await executeTool(step.tool, step.args);
    plan = await llm.reflect(plan, result);

    if (plan.complete) break;
  }

  return plan.summary;
}

The key insight is that agents are iterative — they don't need to get everything right on the first try.

When to Use Agents

Agents shine when the task involves:

1. Ambiguity — the exact steps aren't known upfront
2. Multiple tools — the task requires coordinating several APIs or systems
3. Error recovery — the agent can detect failures and retry or adjust

Common Pitfalls

Over-delegation

Don't hand an agent a task that a simple script could solve. Agents add latency and non-determinism.

Missing guardrails

Always constrain what tools an agent can access. A code-writing agent shouldn't have production database credentials.

Ignoring observability

Log every action and decision. When an agent misbehaves, you need a clear trace to debug it.

What's Next

In the next article, we'll walk through building a production-ready agent that handles pull request reviews — including tool design, prompt engineering, and evaluation.