How to Build an Autonomous AI Agent on a Mac Mini

Most people start with prompts. Start with architecture instead. A reliable autonomous AI agent is an operating system: identity, memory, execution, and safeguards.

1. Define the Role Boundary Before You Write Any Code

Pick one job for the agent. Examples: support triage, content ops, research assistant, bot manager. Scope creep kills autonomy because every new role introduces unclear handoffs.

Use plain-language files to lock behavior:

SOUL.md: voice, values, and non-negotiable behavior.
AGENTS.md: execution instructions, file and safety rules, and workflow constraints.
USER.md: who the human partner is and what context matters.

2. Build Memory as a File System, Not as Hope

Stateless sessions are normal. Durable memory is your responsibility. Keep three layers:

Daily logs: one file per day for facts and outcomes.
Active memory: one MEMORY.md for current priorities.
Lessons learned: one .learnings/LEARNINGS.md for recurring mistakes and hard-won fixes.

3. Use a Task Queue With Small, Single-Concern Briefs

Give the agent one concern per task. Avoid bundle tickets like “fix bug + redesign + docs”. The brief should include context, requirements, acceptance criteria, and constraints.

Task brief quality checklist:

One clear objective
Concrete acceptance criteria
Known constraints and forbidden files
Definition of done signal

4. Add Guardrails for External Actions

Anything that leaves the machine needs stricter checks: money movement, public posting, client comms, and credentials. Require explicit verification before each class of action.

Store credentials in Keychain, not config files.
Log sensitive workflows in runbooks.
Prefer dry-run paths where possible.

5. Instrument the System Like Production Software

Autonomy without observability is gambling. Add lightweight checks:

Health cron for failed tasks and stalled queues.
Error monitor that escalates repeated failures.
Daily summary with outputs, blocked items, and next actions.

6. Optimize for Trust, Not Just Speed

Fast output is easy. Reliable output builds a business. Users trust agents that are transparent about tradeoffs, track mistakes, and avoid repeating known failures.

The fastest route to trust is simple: document every failure once, enforce the fix forever.