Phase 0: Long-Horizon Autonomy · ~45 minutes · Python (stdlib · two-stage classifier simulator)

Permission Modes for Autonomous Agents

A permission ladder — graduated levels of autonomy from review-every-action to approve-everything — is how a harness governs what an autonomous agent may do without asking.

Hiring signal: Can operate permission modes for autonomous agents in production

Introduction

Claude Code, this lesson's worked example, exposes six such modes: "plan" asks before every action, "default" (labeled "Manual" in the UI) asks only for risky ones, "acceptEdits" auto-approves file writes but still confirms shell execution, and "bypassPermissions" approves everything. Auto Mode — the auto permission mode — replaces per-action approval with a separate classifier model that reviews each action before it runs and blocks anything that escalates beyond what the request asked for. Action budgets are enforced via max_turns and max_budget_usd. Availability of auto depends on plan, org enablement, model, and provider — and Anthropic is explicit that the classifier is not sufficient alone.

Type: Learn Languages: Python (stdlib, two-stage classifier simulator) Prerequisites: Phase 15 · 01 (Long-horizon agents), Phase 15 · 09 (Coding-agent landscape) Time: ~45 minutes

Objective

Learning objectives

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Agent