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Guide 3 min read • Updated: November 2026

AI Agents, ARI & MCP: The Complete Guide

Agents become useful when they can see the real plan and dangerous when they can change it unchecked. These articles cover connecting agents over MCP, briefing them with work orders, managing them beside people, and the guardrails that keep them safe.

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CoMng.AI Project Intelligence Lab
Facts checked against product documentation · editorial standards
Short answer

Using AI agents in project management safely means giving them a live plan, a scoped identity and an approval path. This guide links every CoMng.AI article on ARI, the MCP server, coding agents, work orders and governance.

Executive Summary & Key Takeaways
  • Who it is for: delivery leaders, PMOs and teams working with AI who want practical, verified guidance.
  • How it is organised: 9 articles, from first principles to detailed how-tos.
  • Where to start: MCP Server for Project Management: Connect Claude and Other Agents.
  • How it is checked: claims are verified against CoMng.AI documentation; illustrations are labelled.

Start Here

If you only read one article, start with MCP Server for Project Management: Connect Claude and Other Agents: Step-by-step: create a scoped API key and connect Claude, Claude Code, Codex CLI, Gemini CLI or Antigravity to your CoMng.AI project over MCP.

All 9 Articles in This Guide

AI Project Assistant ARI: Live Context, Human Approval
Meet ARI, the in-project AI assistant: it reads live tasks, milestones, budget and decisions, drafts change requests and notes, and never applies a change without approval.
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ARI: The In-Project Teammate for Governed Project Execution
A generic chatbot knows nothing about your project and cannot act on it. See how an in-project AI teammate differs on context, action, governance and memory.
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The MCP Bridge: Connecting LLMs to Your Active Project Command Hub
The Model Context Protocol lets AI assistants read and write live project data. See how CoMng.AI exposes tasks, notes, dependencies and Pulse over MCP safely.
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MCP Server for Project Management: Connect Claude and Other Agents
Step-by-step: create a scoped API key and connect Claude, Claude Code, Codex CLI, Gemini CLI or Antigravity to your CoMng.AI project over MCP.
Read article →
Claude Code Project Management: Give It a Live Plan
Stop pasting context into your coding agent. Connect Claude Code to a live project plan over MCP: pick up tasks, follow acceptance criteria, report progress, stay in scope.
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AI Workplan Export: Bridging Strategy Directly to the CLI
Turn a project plan into an AI-ready work order: Create Workplan for AI, task and branch briefs, MCP connection instructions and progress reporting for CLI agents.
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Manage AI Coding Agents and Humans in One Team Roster
Treat AI coding agents like team members: one roster, shared workload view, work orders and approval gates. See how CoMng.AI manages humans and agents together.
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The Hybrid Workforce: Managing Humans and AI Agents in One OS
How to run a hybrid workforce of people and AI agents: shared roster, role clarity with RACI, capacity planning, governed execution and one source of truth.
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Safe Governance: Why Deterministic Guardrails Are Mandatory for AI
AI that edits your project needs guardrails that do not depend on the model behaving. See CoMng.AI approval gates, bulk-change detection, API key scoping and audit logs.
Read article →

About CoMng.AI

CoMng.AI is an AI-native project management platform that generates a project's plan, tasks, risks and budget from a description or document, monitors project health in real time, and gives both human teams and AI coding agents live access to the same project data, with a person approving every AI-generated change.

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Frequently Asked Questions

What is in the AI Agents, ARI and MCP guide?

Run AI agents and humans together: ARI the in-project assistant, the MCP server, Claude Code workflows, work orders, governance guardrails and hybrid team management.

Are the screenshots real?

Screens labelled "Real CoMng.AI screenshot" are captured from the product. Illustrations labelled "Illustrative UI" are mock-ups built from the product's documented labels and behaviour, with sample data.

Where do these articles get their facts?

From CoMng.AI product documentation and behaviour. See the editorial standards page for how articles are written and checked.

Related reading

Project Risk, Health & Capacity: The Complete GuideRead article →Planning, Scope & Change Control: The Complete GuideRead article →Migration & Enterprise: The Complete GuideRead article →
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