Autonomous Agents

Introduction

Autonomous agents run without a human in the loop for every step — only checked at milestones. They unlock huge value (long tasks, parallel work) but also bring risk (cost, mistakes, safety).

Beginner analogy: instead of asking your assistant for confirmation on every email, you trust them with a daily summary at 5pm.

Understanding the topic

Core concepts:

• Human-in-the-loop: confirm every step (safest, slowest).
• Human-on-the-loop: confirm at milestones (balanced).
• Fully autonomous: only confirm exceptions (fastest, risky).
• Sandbox dangerous tools (shell, payments) by default.
• Always have a kill switch.

Syntax reference

Visual workflow / architecture:

┌────────────┐
│ User goal  │
└──────┬─────┘
       ▼
  Agent run (10-100 steps)
       │
       ▼
  Milestone? ── yes ──► Human check
       │
       no
       ▼
   Continue → Final report

Real-world use

AutoGPT pioneered autonomy; Devin runs hours unsupervised; Operator asks for confirmation on payments.

Best practices

• Start human-in-loop, graduate to on-loop after evals.
• Sandbox all destructive tools.

Common mistakes

• Going fully autonomous on day one — you'll have a horror story.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Three autonomy levels.
• Q2. When is fully autonomous appropriate?
• Q3. Scenario: your agent racked up $400 in tool fees overnight. Two policies to prevent it?