AI Agents Architecture

What You Will Learn

AI agents combine model reasoning with tools, workflows, memory, and review. This guide explains how to design agents that are useful, controlled, and production-friendly.

Prerequisites

• LLM fundamentals
• API design basics
• Understanding of business workflows

Concept Overview

An agent receives a goal, decides what steps to take, uses tools when needed, and returns a result. The key engineering challenge is giving enough autonomy to be useful without losing control.

Step-by-Step Explanation

1. Define the agent goal narrowly.
2. List allowed tools and permissions.
3. Define the planning and execution loop.
4. Add memory only for information the agent truly needs later.
5. Add stop conditions and budgets.
6. Validate tool inputs and outputs.
7. Add human approval for high-risk actions.
8. Log decisions for debugging and audits.

Code Example

const allowedTools = new Set(["searchDocs", "createDraft"]);

async function runAgent(goal: string) {
  for (let step = 0; step < 5; step += 1) {
    const action = await planner.nextAction({ goal, allowedTools: [...allowedTools] });

    if (action.type === "finish") {
      return action.answer;
    }

    if (!allowedTools.has(action.toolName)) {
      throw new Error(`Tool not allowed: ${action.toolName}`);
    }

    await tools[action.toolName](action.input);
  }

  throw new Error("Agent stopped after reaching the step budget");
}

Real-World Use Cases

• Ticket triage
• Deployment assistants
• Research workflows
• Internal operations automation
• Knowledge base maintenance

Best Practices

• Prefer narrow agents over general agents.
• Keep tools explicit and permissioned.
• Add time, token, and action limits.
• Require review before destructive or financial actions.
• Make agent state visible for debugging.
• Test with realistic failure cases.

Common Mistakes

• Giving an agent too many tools
• Allowing unbounded loops
• Trusting model output without validation
• Skipping audit logs
• Treating an agent as a replacement for product workflow design

Interview Questions

• What is an AI agent?
• How is an agent different from a chatbot?
• Why do agents need tool boundaries?
• What is human-in-the-loop review?
• How do you prevent runaway agent behavior?

Summary

Good agents are constrained systems. Clear goals, limited tools, validation, budgets, and human review make them useful in real products.