# 🤖 AI Agents Module (Module Idea)

## 🧭 Overview

The **AI Agents module** provides visibility, monitoring, and operational control over **AI coding agents running on a server**. It acts as a centralized dashboard for observing agent sessions, tracking actions performed by AI, and managing AI-driven development workflows.

This module is designed for environments where developers run tools such as:

- Claude Code
- OpenAI Codex (CLI / App)
- OpenCode
- Gemini CLI
- Aider and similar terminal agents
- Custom AI agents

Many of these tools operate as **terminal-based agents capable of reading files, modifying code, and executing commands** inside a repository or working directory. ([developers.openai.com](https://developers.openai.com/codex/cli/?utm_source=chatgpt.com))

The module provides **live observability, session monitoring, and management tools** for these agents.

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# 🎯 Goals of the Module

The AI Agents module should allow ServerMon to:

- Detect **active AI agent sessions** running on the server
- Provide visibility into **what the agent is doing**
- Track **code and filesystem changes made by agents**
- Monitor **resource usage and process activity**
- Provide **auditability and debugging** for AI‑driven workflows

Modern coding agents can autonomously write features, fix bugs, modify repositories, run tests, and even propose pull requests. ([openai.com](https://openai.com/index/introducing-codex/?utm_source=chatgpt.com))

Because of this autonomy, **observability and monitoring become essential** when running agents in production environments.

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# 📡 Agent Session Discovery

The module should automatically detect running AI agent processes.

Possible detection methods:

- Known CLI processes
- Running agent daemons
- Agent log directories
- Session databases
- Running container workloads

Detected tools may include:

- Claude Code sessions
- Codex CLI sessions
- OpenCode agents
- Custom agent frameworks

Each detected session should appear in the **AI Agents dashboard**.

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# 📊 Agent Sessions Dashboard

The main dashboard displays **all active and recent AI agent sessions**.

Each session row/card should display:

### 🤖 Agent Identity

- Agent type (Claude Code / Codex / OpenCode / custom)
- Agent version
- Model used (GPT, Claude, Gemini etc.)

Some agent systems allow switching models or providers dynamically. ([github.com](https://github.com/opencode-ai/opencode?utm_source=chatgpt.com))

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### 🧑 Session Owner

- System user
- initiating developer
- API key / agent identity

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### 📂 Working Environment

- working directory
- repository name
- git branch
- container / host

Many coding agents operate **directly within a repository and modify multiple files across the project**. ([northflank.com](https://northflank.com/blog/claude-code-vs-openai-codex?utm_source=chatgpt.com))

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### 🕒 Session Lifecycle

- start time
- last activity
- session duration

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### ⚡ Current Activity

Examples:

- editing files
- analyzing repository
- running tests
- generating code
- executing shell commands

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### 🟢 Session Status

- Running
- Idle
- Waiting for user input
- Error
- Completed

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# 🕘 Past Session Inspection

The module should allow inspection of **previous (completed) AI agent sessions**, not only currently running ones.

Historical sessions are important for:

- debugging failures
- auditing AI actions
- reviewing what code changes were made
- understanding how an agent solved a task

The **Agent Sessions Dashboard** should therefore include:

- Active sessions
- Recent sessions
- Historical sessions

Possible filters:

- running
- completed
- failed
- time range

Historical sessions should retain key metadata:

- agent type
- session owner
- repository / working directory
- start and end time
- duration
- final status

Selecting a past session should open the same **Session Detail View** used for live sessions, but in **read‑only inspection mode**.

Stored information may include:

- conversation history
- action timeline
- files modified
- commands executed
- logs produced during the session

This allows teams to **review and audit agent behavior after the session has finished**.

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# 📜 Session Detail View

Clicking a session opens a **deep inspection panel**.

This allows administrators and developers to see exactly what the agent has done.

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## 💬 Prompt / Conversation History

If available, show:

- user prompts
- agent responses
- reasoning summaries

Many agent tools persist conversations or sessions to local storage or databases. ([github.com](https://github.com/opencode-ai/opencode?utm_source=chatgpt.com))

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## 🧾 Action Timeline

A chronological timeline of agent actions:

Example timeline:

1. opened repository
2. searched codebase
3. edited files
4. ran tests
5. created commit

Structured logs like this are used in agent frameworks to reconstruct sessions and debug behavior. ([arxiv.org](https://arxiv.org/abs/2412.08445?utm_source=chatgpt.com))

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## 📂 Files Modified

Track files modified by the AI agent during the session.

Example:

- `src/server.ts`
- `package.json`
- `README.md`

Optional enhancements:

- show diff preview
- link to file browser

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## 🖥 Commands Executed

Many agents run shell commands automatically.

Examples:

- `npm test`
- `git commit`
- `pytest`

The module should capture and display **commands executed by the agent**.

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## 🧾 Logs & Output

Real‑time streaming output:

- agent logs
- tool output
- command output

This helps debug failures or runaway agent loops.

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# ⚙️ Agent Controls

Allow administrators to manage the **lifecycle of running agent processes**.

Because many AI agents run as CLI processes, direct pause/resume capabilities may not always be supported. Instead, the module should provide **process‑level lifecycle management**.

Supported lifecycle controls may include:

- ⛔ **Terminate session** – gracefully stop the running agent session
- 💀 **Kill process** – forcefully stop a stuck or runaway agent
- 🔄 **Restart agent session** – restart the agent process in the same working directory
- ▶ **Start new session** – launch a new agent instance

Optional capabilities:

- ✉ **Send instruction** – send additional prompts or commands to the agent if supported
- 🧾 **Attach note / annotation** – add notes for debugging or auditing sessions
- 🛑 **Stop current task** – request the agent to halt the current operation (if the agent API supports it)

These controls allow administrators to safely manage **long‑running or malfunctioning AI agent sessions**.

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# 📊 Resource Monitoring

Display system resource usage per session:

- CPU usage
- memory usage
- disk I/O
- network traffic

This helps detect:

- runaway reasoning loops
- excessive compute usage
- stalled agents

Agent observability research shows monitoring both **high‑level intent and low‑level system actions** is important for debugging agent behavior. ([arxiv.org](https://arxiv.org/abs/2508.02736?utm_source=chatgpt.com))

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# 📁 Project Awareness

If the agent operates inside a Git repository:

Display repository context:

- current branch
- files modified
- staged changes
- commits created by the agent

This helps teams understand **exactly what changes the AI agent introduced**.

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# 🧩 Multi‑Agent Workflows

Some platforms allow **multiple agents to run tasks in parallel across projects**. ([intuitionlabs.ai](https://intuitionlabs.ai/articles/openai-codex-app-ai-coding-agents?utm_source=chatgpt.com))

The module should support:

- viewing multiple agents per project
- grouping agents by repository
- tracking parallel workflows

Example:

Project: `servermon`

Agents running:

- coding agent
- testing agent
- documentation agent

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# 🔗 Integration With Other ServerMon Modules

### 📂 File Browser

Open files modified by agents directly in the file browser.

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### 🖥 Terminal Module

Open the exact working directory in a terminal.

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### 🧾 Logs Module

View logs associated with agent processes.

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# 📊 Agent Analytics

Aggregate statistics across all agent sessions.

Possible metrics:

- total agent sessions
- most active repositories
- files modified
- commits generated
- average session duration

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# 🚀 Future Capabilities

## 🧠 Agent Memory Tracking

Track persistent memory used by long‑running agents.

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## 🧾 Session Replay

Replay agent sessions step‑by‑step.

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## 🧩 Agent Skill Visualization

Show which tools the agent used:

- git
- shell
- test runner
- package manager

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## 🛡 Safety Monitoring

Detect risky behaviors:

- large repository rewrites
- deleting critical files
- infinite reasoning loops

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# 🧩 Internal Architecture (Agent Submodules)

## 🧠 Submodule Philosophy

Each AI tool within this module should be implemented as its own **submodule**.

This architectural decision is driven by the fact that **each tool has a different working philosophy** and interaction model. By treating each agent (Claude Code, Codex, OpenCode, etc.) as an independent submodule, the system becomes:

- **Easier to manage**: Logic for one agent doesn't bleed into another.
- **Maintainable**: Updates to one tool's CLI or API only require changes in its specific submodule.
- **Scalable**: New agents can be added by creating a new submodule without touching existing ones.

To start with, this submodule approach will be implemented for:

- **Claude Code**
- **Codex**
- **OpenCode**

The top-level AI Agents module provides **shared infrastructure and UI**, while each agent-specific submodule handles **agent-specific detection and data extraction**.

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## 🏗 Architecture Layers

### 1️⃣ Core AI Agents Module

The top module provides reusable functionality shared across all agents.

Responsibilities:

- unified **Agent Sessions Dashboard**
- shared **session data model**
- **UI components** for session inspection
- **resource monitoring integration**
- **session history storage**
- integration with other ServerMon modules

The core module defines a **standard interface** that all agent submodules must implement.

Example interface responsibilities:

- detect running sessions
- extract session metadata
- collect logs
- collect prompts / responses
- track file changes

This allows the UI to work with **all agents in a consistent way**.

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### 2️⃣ Agent-Specific Submodules

Each supported AI agent should have its own implementation module.

Initial agents to support:

- **Claude Code Adapter**
- **Codex Adapter**
- **OpenCode Adapter**

Each adapter is responsible for:

- detecting sessions for that specific agent
- parsing agent logs or session files
- extracting prompts / responses
- identifying commands executed
- identifying modified files

Because agent tools evolve quickly, this design allows **new agents to be added without modifying the core module**.

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### 3️⃣ Plugin-Style Extensibility

The architecture should allow additional agent integrations in the future.

Example future adapters:

- Gemini CLI
- Aider
- Devika
- custom internal agents

New agents can be added by implementing the **agent adapter interface** defined by the core module.

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## 🔁 Shared Components

To maximize reuse, the following systems should remain inside the **core AI Agents module**:

Shared components:

- session dashboard UI
- session detail viewer
- action timeline renderer
- log streaming viewer
- resource monitoring widgets
- session history storage

Agent submodules should only provide:

- detection logic
- parsing logic
- session metadata extraction

This separation ensures the **UI and analytics systems remain reusable across all agents**.

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## 📦 Example Architecture

Conceptual structure:

```
ai-agents-module

core/
  session-manager
  session-model
  dashboard-ui
  session-detail-ui
  analytics

adapters/
  claude-code/
  codex/
  opencode/

shared/
  log-streamer
  timeline-renderer
  diff-viewer
```

This architecture keeps the system **modular, maintainable, and easy to extend**.

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# 🎨 User Interface Design

The **AI Agents module UI** should follow the same design principles used across existing ServerMon modules:

- Clean dashboard layout
- Fast real‑time updates
- Consistent topbar + content structure
- Rich icons and status indicators
- Context panels instead of full page navigation

The goal is to make AI sessions **easy to observe at a glance while still allowing deep inspection**.

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## 🧭 Module Layout

The module should be organized into three primary UI areas.

### 1️⃣ Topbar

Consistent with other modules.

Topbar elements:

- 🔄 Refresh sessions
- 🔍 Session search
- 🧠 Filter by agent type
- 📂 Filter by repository
- ⚙ Open module settings

Optional indicators:

- total running agents
- idle agents
- error sessions

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### 2️⃣ Agent Sessions List

The primary dashboard view.

Sessions should appear as **table rows or cards** depending on screen size.

Each session card should include:

- 🤖 agent icon
- agent type
- repository / directory
- model used
- session owner
- status indicator
- running time

Status indicators should be color coded:

- 🟢 running
- 🟡 idle
- 🔵 waiting
- 🔴 error

Clicking a session opens the **Session Detail View**.

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### 3️⃣ Session Detail Panel

Opening a session reveals a **rich inspection interface**.

Suggested layout:

Left sidebar:

- session metadata
- resource usage
- project context

Main content tabs:

- 💬 Conversation
- 🧾 Action timeline
- 📂 Files modified
- 🖥 Commands executed
- 📜 Logs / output

Tabs allow developers to quickly inspect **what the agent is doing internally**.

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## 📊 Live Visual Indicators

The UI should use subtle indicators to show activity.

Examples:

- animated "thinking" indicator when the agent is reasoning
- streaming output indicator
- git activity badges

This helps users immediately understand whether the agent is:

- actively working
- waiting for input
- stalled

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# ⚙️ Module Settings

The **AI Agents module settings** should follow the same configuration approach used by other ServerMon modules (such as the Terminal module).

Settings allow administrators to configure how agent detection and monitoring works.

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## 🔎 Agent Detection Settings

Configure how ServerMon detects agent sessions.

Options may include:

- enable automatic process scanning
- define known agent process names
- monitor specific directories
- detect containerized agents

Example monitored processes:

- `claude`
- `codex`
- `opencode`
- `aider`

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## 📂 Repository Awareness

Optional configuration for Git integrations.

Settings:

- enable repository detection
- enable commit tracking
- enable diff preview

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## 📊 Resource Monitoring Settings

Control how resource usage is collected.

Options:

- enable CPU monitoring
- enable memory monitoring
- update interval (1s / 3s / 5s)

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## 📜 Log Collection

Configure how agent logs are captured.

Options:

- monitor agent log directories
- enable live log streaming
- maximum log retention

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## 🧠 Advanced Settings

Future options could include:

- enable session replay
- enable analytics collection
- enable multi‑agent workflow detection

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# 🎯 Summary

The **AI Agents module** turns ServerMon into an **AgentOps platform** for AI‑driven development.

Instead of treating AI tools as opaque black boxes, this module provides:

- observability
- auditing
- debugging
- operational control

for modern AI coding agents running on the server.