The Road to Agent Autonomy: Challenges, Discoveries, and a 28-Line Solution

Source: Dev.to

The Problem with Existing Agent Configurations

Some CLAUDE.md or AGENTS.md files I’ve seen are micromanagement dressed up as engineering: hundreds of lines dictating which files to read, which commands to run, and what order to do things in.
These files can consume thousands of tokens before the agent even starts thinking about the actual task.

• Every token spent on instructions is a token the agent can’t spend on your work.
• The more rigid the instructions, the more brittle the agent becomes.

A 28‑Line Operating Law for Autonomous Agents

I wanted to give an agent a task in Claude Code terminal and have it complete the task without any input or feedback, no matter how easy or complex. The result is a concise set of directives that keep the agent in execution mode and provide a simple persistence mechanism.

Preventing Plan Mode

The agent often enters plan mode, voluntarily switching itself to read‑only, spawning research sub‑agents, and never writing any files. This is caused by a prompt injection that prepends a reminder like “you MUST NOT make any edits.” The write tools are still available; the agent simply chooses not to use them.

Fix (one line):

Never use EnterPlanMode. Execute tasks directly.

Even though EnterPlanMode isn’t a real tool name, the agent understands the intent and stays in execution mode.

Providing Persistent Memory

Agents have no memory between sessions; each new session starts fresh. To give the agent a place to persist information, add two more lines:

You have no lasting memory between sessions. Use .memory-bank in project root for persistence.
Read .memory-bank at start, write to it throughout. Goal, success measures, plan, and progress must exist there before and during execution.

Now the agent knows:

• It lacks lasting memory.
• Where to store persistent data (.memory-bank).
• What to store (goal, success measures, plan, progress).

The agent decides how to organise those files, rather than being forced into a rigid structure.

Experiments and Results

Baseline (no changes)

• Task: Build a full‑stack app.
• Permissions: --dangerously-skip-permissions.
• Outcome after 34 minutes: Zero files created.
• The agent was stuck in plan mode, performing research with three sub‑agents but never writing to disk.

After Adding the “Never use EnterPlanMode” Line

• The agent remained in execution mode and began building immediately.

After Adding the .memory-bank Lines

• First action: read .memory-bank, find a clean slate.
• Then: write goal, success measures, and an architecture plan.
• Followed by building the database, API adapters, routes, server, and full frontend—all within minutes.

Key Insights

• Shift from control to delegation. Instead of enumerating every step, describe the desired outcome, boundaries, and let the model reason.
• Frontier models infer and make judgment calls. Over‑constraining them with rigid instructions fills their context with rules rather than understanding.
• A concise “constitution” works better than a checklist. Providing a short, high‑level set of principles enables the agent to organise its own workflow efficiently.

The full Autonomous Agent Law and the reasoning behind each line are available on GitHub. Feel free to fork, test, and observe the changes.