---
name: autonomous-agent-harness-9ff931c4e4dc
description: Transform OpenAI Codex into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging OpenAI Codex's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
origin: ecc
---

# Autonomous Agent Harness

Turn OpenAI Codex into a persistent, self-directing agent system using only native features and MCP servers.

## Consent and Safety Boundaries

Autonomous operation must be explicitly requested and scoped by the user. Do not create schedules, dispatch remote agents, write persistent memory, use computer control, post externally, modify third-party resources, or act on private communications unless the user has approved that capability and the target workspace for the current setup.

Prefer dry-run plans and local queue files before enabling recurring or event-driven actions. Keep credentials, private workspace exports, personal datasets, and account-specific automations out of reusable ecc artifacts.

## When to Activate

- User wants an agent that runs continuously or on a schedule
- Setting up automated workflows that trigger periodically
- Building a personal AI assistant that remembers context across sessions
- User says "run this every day", "check on this regularly", "keep monitoring"
- Wants to replicate functionality from Hermes, AutoGPT, or similar autonomous agent frameworks
- Needs computer use combined with scheduled execution

## Architecture

```
┌──────────────────────────────────────────────────────────────┐
│                    OpenAI Codex Runtime                        │
│                                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────────┐ │
│  │  Crons   │  │ Dispatch │  │ Memory   │  │ Computer    │ │
│  │ Schedule │  │ Remote   │  │ Store    │  │ Use         │ │
│  │ Tasks    │  │ Agents   │  │          │  │             │ │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └──────┬──────┘ │
│       │              │             │                │        │
│       ▼              ▼             ▼                ▼        │
│  ┌──────────────────────────────────────────────────────┐    │
│  │              ecc Skill + Agent Layer                  │    │
│  │                                                      │    │
│  │  skills/     agents/     commands/     hooks/        │    │
│  └──────────────────────────────────────────────────────┘    │
│       │              │             │                │        │
│       ▼              ▼             ▼                ▼        │
│  ┌──────────────────────────────────────────────────────┐    │
│  │              MCP Server Layer                        │    │
│  │                                                      │    │
│  │  memory    github    exa    supabase    browser-use  │    │
│  └──────────────────────────────────────────────────────┘    │
└──────────────────────────────────────────────────────────────┘
```

## Core Components

### 1. Persistent Memory

Use OpenAI Codex's built-in memory system enhanced with MCP memory server for structured data.

**Built-in memory** (`~/.codex/projects/*/memory/`):
- User preferences, feedback, project context
- Stored as markdown files with frontmatter
- Automatically loaded at session start

**MCP memory server** (structured knowledge graph):
- Entities, relations, observations
- Queryable graph structure
- Cross-session persistence

**Memory patterns:**

```
# Short-term: current session context
Use TodoWrite for in-session task tracking

# Medium-term: project memory files
Write to ~/.codex/projects/*/memory/ for cross-session recall

# Long-term: MCP knowledge graph
Use mcp__memory__create_entities for permanent structured data
Use mcp__memory__create_relations for relationship mapping
Use mcp__memory__add_observations for new facts about known entities
```

### 2. Scheduled Operations (Crons)

Use OpenAI Codex's scheduled tasks to create recurring agent operations.

**Setting up a cron:**

```
# Via MCP tool
mcp__scheduled-tasks__create_scheduled_task({
  name: "daily-pr-review",
  schedule: "0 9 * * 1-5",  # 9 AM weekdays
  prompt: "Review all open PRs in mehmet-turac/everything-openai-codex. For each: check CI status, review changes, flag issues. Post summary to memory.",
  project_dir: "/path/to/repo"
})

# Via codex -p (programmatic mode)
echo "Review open PRs and summarize" | codex -p --project /path/to/repo
```

**Useful cron patterns:**

| Pattern | Schedule | Use Case |
|---------|----------|----------|
| Daily standup | `0 9 * * 1-5` | Review PRs, issues, deploy status |
| Weekly review | `0 10 * * 1` | Code quality metrics, test coverage |
| Hourly monitor | `0 * * * *` | Production health, error rate checks |
| Nightly build | `0 2 * * *` | Run full test suite, security scan |
| Pre-meeting | `*/30 * * * *` | Prepare context for upcoming meetings |

### 3. Dispatch / Remote Agents

Trigger OpenAI Codex agents remotely for event-driven workflows.

**Dispatch patterns:**

```bash
# Trigger from CI/CD
curl -X POST "https://api.openai.com/dispatch" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{"prompt": "Build failed on main. Diagnose and fix.", "project": "/repo"}'

# Trigger from webhook
# GitHub webhook → dispatch → Codex agent → fix → PR

# Trigger from another agent
codex -p "Analyze the output of the security scan and create issues for findings"
```

### 4. Computer Use

Leverage Codex's computer-use MCP for physical world interaction.

**Capabilities:**
- Browser automation (navigate, click, fill forms, screenshot)
- Desktop control (open apps, type, mouse control)
- File system operations beyond CLI

**Use cases within the harness:**
- Automated testing of web UIs
- Form filling and data entry
- Screenshot-based monitoring
- Multi-app workflows

### 5. Task Queue

Manage a persistent queue of tasks that survive session boundaries.

**Implementation:**

```
# Task persistence via memory
Write task queue to ~/.codex/projects/*/memory/task-queue.md

# Task format
---
name: task-queue
type: project
description: Persistent task queue for autonomous operation
---

## Active Tasks
- [ ] PR #123: Review and approve if CI green
- [ ] Monitor deploy: check /health every 30 min for 2 hours
- [ ] Research: Find 5 leads in AI tooling space

## Completed
- [x] Daily standup: reviewed 3 PRs, 2 issues
```

## Replacing Hermes

| Hermes Component | ecc Equivalent | How |
|------------------|---------------|-----|
| Gateway/Router | OpenAI Codex dispatch + crons | Scheduled tasks trigger agent sessions |
| Memory System | Codex memory + MCP memory server | Built-in persistence + knowledge graph |
| Tool Registry | MCP servers | Dynamically loaded tool providers |
| Orchestration | ecc skills + agents | Skill definitions direct agent behavior |
| Computer Use | computer-use MCP | Native browser and desktop control |
| Context Manager | Session management + memory | ecc 2.0 session lifecycle |
| Task Queue | Memory-persisted task list | TodoWrite + memory files |

## Setup Guide

### Step 1: Configure MCP Servers

Ensure these are in `~/.codex.json`:

```json
{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@openai/memory-mcp-server"]
    },
    "scheduled-tasks": {
      "command": "npx",
      "args": ["-y", "@openai/scheduled-tasks-mcp-server"]
    },
    "computer-use": {
      "command": "npx",
      "args": ["-y", "@openai/computer-use-mcp-server"]
    }
  }
}
```

### Step 2: Create Base Crons

```bash
# Daily morning briefing
codex -p "Create a scheduled task: every weekday at 9am, review my GitHub notifications, open PRs, and calendar. Write a morning briefing to memory."

# Continuous learning
codex -p "Create a scheduled task: every Sunday at 8pm, extract patterns from this week's sessions and update the learned skills."
```

### Step 3: Initialize Memory Graph

```bash
# Bootstrap your identity and context
codex -p "Create memory entities for: me (user profile), my projects, my key contacts. Add observations about current priorities."
```

### Step 4: Enable Computer Use (Optional)

Grant computer-use MCP the necessary permissions for browser and desktop control.

## Example Workflows

### Autonomous PR Reviewer
```
Cron: every 30 min during work hours
1. Check for new PRs on watched repos
2. For each new PR:
   - Pull branch locally
   - Run tests
   - Review changes with code-reviewer agent
   - Post review comments via GitHub MCP
3. Update memory with review status
```

### Personal Research Agent
```
Cron: daily at 6 AM
1. Check saved search queries in memory
2. Run Exa searches for each query
3. Summarize new findings
4. Compare against yesterday's results
5. Write digest to memory
6. Flag high-priority items for morning review
```

### Meeting Prep Agent
```
Trigger: 30 min before each calendar event
1. Read calendar event details
2. Search memory for context on attendees
3. Pull recent email/Slack threads with attendees
4. Prepare talking points and agenda suggestions
5. Write prep doc to memory
```

## Constraints

- Cron tasks run in isolated sessions — they don't share context with interactive sessions unless through memory.
- Computer use requires explicit permission grants. Don't assume access.
- Remote dispatch may have rate limits. Design crons with appropriate intervals.
- Memory files should be kept concise. Archive old data rather than letting files grow unbounded.
- Always verify that scheduled tasks completed successfully. Add error handling to cron prompts.


---
Source attribution: mturac
Original project: https://github.com/mturac/everything-openai-codex/blob/b5057da5f42ed6b12cd3a59e89af0ccd12cff7c2/skills/autonomous-agent-harness/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Mehmet Turac

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


## Additional upstream notices

autonomous-agent-harness-9ff931c4e4dc/DAVEAI-UPSTREAM-NOTICE.md

# Notices and Acknowledgements

Everything OpenAI Codex is a Codex-focused adaptation inspired by the public
[`affaan-m/everything-claude-code`](https://github.com/affaan-m/everything-claude-code)
project.

Respect to Affaan Mustafa and the contributors to that project for making the
multi-harness agent operating-system pattern visible in the open.

This repository adapts the public idea and workflow surface for OpenAI Codex:
names, defaults, install targets, docs, validators, and cross-harness guidance
are maintained here for the Codex-oriented project.


# Mega AI Hub resource metadata

Creator / publisher: mturac
Original project: https://github.com/mturac/everything-openai-codex/blob/b5057da5f42ed6b12cd3a59e89af0ccd12cff7c2/skills/autonomous-agent-harness/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering, Product Management
Roles: DevOps / SRE, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API, Backend
Tools: GitHub, Openai Codex, Supabase
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 15644cccdc79e60a9eaedb7d7843311d30a5b51cf57f50f0984842afcacff42c