Agentic Os

Agentic OS

Treat OpenAI Codex as a persistent runtime / operating system rather than a chat session. This skill codifies the architecture used by production agentic setups: a kernel config that routes tasks to specialist agents, persistent file-based memory, scheduled automation, and a JSON/markdown data layer.

When to Activate

• Building a multi-agent workflow inside OpenAI Codex
• Setting up persistent OpenAI Codex automation that survives session restarts
• Creating a "personal OS" or "agentic OS" for recurring tasks
• User says "agentic OS", "personal OS", "multi-agent", "agent coordinator", "persistent agent"
• Structuring long-running projects where context must survive across sessions

Architecture Overview

The Agentic OS has four layers. Each layer is a directory in your project root.

project-root/
├── CODEX.md          # Kernel: identity, routing rules, agent registry
├── agents/            # Specialist agent definitions (markdown prompts)
├── .codex/commands/  # Slash commands: user-facing CLI
├── scripts/           # Daemon scripts: scheduled or event-driven tasks
└── data/              # State: JSON/markdown filesystem, no external DB

Layer Responsibilities

The Kernel

CODEX.md is the kernel. It acts as the COO / orchestrator. Codex reads it at session start and uses it to route work.

Kernel Structure

# CODEX.md - Agentic OS Kernel

## Identity
You are the COO of [project-name]. You route tasks to specialist agents.
You never write code directly. You delegate to the right agent and synthesize results.

## Agent Registry

| Agent | Role | Trigger |
|---|---|---|
| @dev | Code, architecture, debugging | User says "build", "fix", "refactor" |
| @writer | Documentation, content, emails | User says "write", "draft", "blog" |
| @researcher | Research, analysis, fact-checking | User says "research", "analyze", "compare" |
| @ops | DevOps, deployment, infrastructure | User says "deploy", "CI", "server" |

## Routing Rules
1. Parse the user request for intent keywords
2. Match to the Agent Registry trigger column
3. Load the corresponding agent file from `agents/<name>.md`
4. Hand off execution with full context
5. Synthesize and present the result back to the user

## Model Policies
- Default model: use the repository or harness default.
- @dev tasks: prefer a higher-reasoning model for complex architecture.
- @researcher tasks: use the configured research-capable model and approved search tools.
- Cost ceiling: warn before exceeding the project's configured spend threshold.

Key Principle

The kernel should be small and declarative. Routing logic lives in plain markdown tables, not code. This makes the system inspectable and editable without debugging.

Specialist Agents

Each agent is a standalone markdown file in agents/. Codex loads the relevant agent file when routing a task.

Agent Definition Format

# @dev - Software Engineer

## Identity
You are a senior software engineer. You write clean, tested, production-grade code.
You prefer simple solutions. You ask clarifying questions when requirements are ambiguous.

## Memory Scope
- Read `data/projects/<current-project>.md` for context
- Read `data/decisions/` for architectural decisions
- Append execution logs to `data/logs/<date>-@dev.md`

## Tool Access
- Full filesystem access within project root
- Git operations (status, diff, commit, branch)
- Test runner access
- MCP servers as configured in `.codex/mcp.json`

## Constraints
- Always write tests for new features
- Never commit directly to `main`; use feature branches
- Prefer editing existing files over creating new ones
- Keep functions under 50 lines when possible

Multi-Agent Collaboration Pattern

When a task spans multiple agents, the kernel runs them sequentially or in parallel:

User: "Build a landing page and write the launch blog post"

Kernel routing:
1. @dev - "Build a landing page with [requirements]"
2. @writer - "Write a launch blog post for [product] using the landing page copy"
3. Kernel synthesizes both outputs into a unified response

For parallel execution, use OpenAI Codex's background task capability or shell scripts that invoke OpenAI Codex with specific agent contexts.

Commands and Daily Workflows

Slash commands are markdown files in .codex/commands/. They define reusable workflows.

Command Structure

# /daily-sync

Run the morning briefing:

1. Read `data/logs/last-sync.md` for context
2. Check project status: `git status`, pending PRs, CI health
3. Review `data/inbox/` for new tasks or decisions needed
4. Generate a summary of blockers, priorities, and next actions
5. Append the briefing to `data/logs/daily/<date>.md`

Standard Command Set

Activating Commands

Place command files in .codex/commands/<command-name>.md. OpenAI Codex auto-discovers them. Users invoke them with /<command-name>.

Persistent Memory

Memory is file-based. No vector DB, no Redis, no PostgreSQL. JSON and markdown files in data/ are the database.

Memory Directory Structure

data/
├── daily-logs/         # Append-only daily activity logs
├── projects/           # Per-project context files
├── decisions/          # Architectural and business decisions (ADR format)
├── inbox/              # New tasks or ideas awaiting triage
├── contacts/           # People, companies, relationship notes
└── templates/          # Reusable prompts and formats

Daily Log Format

# 2026-04-22 - Daily Log

## Sessions
- 09:00 - Session 1: Refactored auth module (@dev)
- 11:30 - Session 2: Drafted investor update (@writer)

## Decisions
- Switched from JWT to session cookies (see `data/decisions/2026-04-22-auth.md`)

## Blockers
- Waiting on API key from vendor (follow up 2026-04-24)

## Next Actions
- [ ] Merge auth refactor PR
- [ ] Send investor update for review

Auto-Reflection Pattern

At the end of each session, the kernel appends a reflection:

## Reflection - Session 3
- What worked: Parallel agent execution saved 20 minutes
- What didn't: @researcher hit a paywalled source, need better source ranking
- What to change: Add `source-tier` field to research notes (A/B/C credibility)

This creates a feedback loop that improves the system over time without code changes.

Scheduled Automation

Agentic OS tasks run on a schedule using external cron, not OpenAI Codex's built-in cron (which dies when the session ends).

macOS: LaunchAgent

<!-- ~/Library/LaunchAgents/com.agentic.daily-sync.plist -->
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" ...>
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.agentic.daily-sync</string>
    <key>ProgramArguments</key>
    <array>
        <string>/codex</string>
        <string>--cwd</string>
        <string>/path/to/project</string>
        <string>--command</string>
        <string>/daily-sync</string>
    </array>
    <key>StartCalendarInterval</key>
    <dict>
        <key>Hour</key>
        <integer>8</integer>
        <key>Minute</key>
        <integer>0</integer>
    </dict>
    <key>StandardOutPath</key>
    <string>/tmp/agentic-daily-sync.log</string>
</dict>
</plist>

Linux: systemd Timer

# ~/.config/systemd/user/agentic-daily-sync.service
[Unit]
Description=Agentic OS Daily Sync

[Service]
Type=oneshot
ExecStart=/usr/local/bin/codex --cwd /path/to/project --command /daily-sync

# ~/.config/systemd/user/agentic-daily-sync.timer
[Unit]
Description=Run daily sync every morning

[Timer] 8:00:00
Persistent=true

[Install]
WantedBy=timers.target

Cross-Platform: pm2

# ecosystem.config.js
module.exports = {
  apps: [{
    name: 'agentic-daily-sync',
    script: 'codex',
    args: '--cwd /path/to/project --command /daily-sync',
    cron_restart: '0 8 * * *',
    autorestart: false
  }]
};

Data Layer

The data layer is your filesystem. Use JSON for structured data and markdown for narrative content.

JSON for Structured State

// data/projects/website-v2.json
{
  "name": "Website v2",
  "status": "in-progress",
  "milestone": "beta-launch",
  "agents_involved": ["@dev", "@writer"],
  "files": {
    "spec": "docs/website-v2-spec.md",
    "design": "designs/website-v2.fig"
  },
  "metrics": {
    "commits": 47,
    "last_session": "2026-04-22T11:30:00Z"
  }
}

Markdown for Narrative

Use markdown for anything a human reads: decisions, logs, research notes, contact records.

Schema Evolution

Never rename existing fields. Add new fields and mark old ones deprecated:

{
  "name": "Website v2",
  "status": "in-progress",
  "milestone": "beta-launch",
  "_deprecated_priority": "high",
  "priority_v2": { "level": "high", "rationale": "Blocks investor demo" }
}

This keeps historical data readable without migration scripts.

Anti-Patterns

Monolithic Single Agent

# BAD - One agent does everything
You are a full-stack developer, writer, researcher, and DevOps engineer.

Split into specialist agents. The kernel handles routing.

Stateless Sessions

# BAD - No memory between sessions
Starting fresh every time OpenAI Codex opens.

Always read data/ at session start and write back at session end.

Hardcoded Credentials

# BAD - API keys in agent files or CODEX.md
Your OpenAI API key is sk-xxxxxxxx

Use environment variables or a .env file loaded by scripts. Agents reference process.env.API_KEY.

External Database for Simple State

# BAD - PostgreSQL for a solo user's agentic OS

Use JSON/markdown files until you have multiple concurrent users or GBs of data.

Over-Engineered Routing

# BAD - Routing logic in code instead of markdown tables
if (intent.includes('deploy')) { agent = opsAgent; }

Keep routing declarative in CODEX.md markdown tables. It is inspectable, editable, and debuggable.

Best Practices

• CODEX.md is under 200 lines and fits in context window
• Each agent file is under 100 lines and focused on one domain
• data/ is git-ignored for sensitive logs, git-tracked for decisions and specs
• Commands use imperative names: /daily-sync, not /run-daily-sync
• Logs are append-only; never edit past daily logs
• Every agent has a Memory Scope section defining what files it reads
• Reflections are written at the end of every session
• Scheduled tasks use external cron (LaunchAgent, systemd, pm2), not OpenAI Codex's session cron
• Cost tracking: log API spend per session in data/logs/<date>-costs.json
• One project = one Agentic OS. Do not share a single CODEX.md across unrelated projects.