MiWork Agents evolves into an AI collaboration workspace

TL;DR MiWork Agents started as a place to talk with AI. It has now evolved into an AI collaboration workspace where people and AI can communicate, organize knowledge, and use work tools in the same environment. This post introduces the upgraded channels, direct messages, wiki, shared chatbots, knowledge bases, MCP connections, and enterprise operations.

Don’t just ask AI. Work with AI.

Enterprise AI usually begins with a private chat: a user asks a question and the AI responds. It is useful, but knowledge stays inside an individual conversation unless it connects to the way a team already works.

Real work also includes discussions with colleagues, shared files, recorded decisions, and actions in business systems. MiWork Agents now brings people, AI, knowledge, and tools into one collaboration environment.

Case Studies: How everyday work changes

The point of this upgrade is not to add one more AI feature. It connects channels, the wiki, knowledge bases, shared chatbots, and MCP so AI can participate in the spaces where people already work. The following two case studies show how the upgraded capabilities connect in practice.

People and AI working together does not mean that AI takes over the work. It combines human experience and judgment with AI's ability to search knowledge and organize context that would otherwise be easy to miss.

Case Study 1. “Why did we decide to build it this way?”

A developer who recently joined the project asks in a channel:

“Why was this feature left out of the mobile version?”

Even the people who participated in the discussion several months earlier may not remember the exact reasoning. A new team member would have to read old conversations and many documents, while an explanation based on memory could omit an important constraint.

The team brings AI into the current conversation. AI uses earlier discussions visible from the current channel and accessible wiki, knowledge-base, and technical documents to organize the original requirements, constraints, and trade-offs. Existing team members verify that the retrieved context matches what happened, while the new member identifies conditions that may have changed.

People decide together whether to retain the original decision or redesign the feature. If the decision changes, AI helps record not only what changed, but why it changed.

Case Study 2. Field and headquarters solve an equipment problem together

A field technician posts equipment photos and symptoms to a channel from a mobile device:

“The vibration is stronger than usual, and the temperature is rising quickly.”

A technical specialist at headquarters asks the technician to check when the noise began, recently replaced parts, and current gauge readings. At the same time, AI searches accessible equipment manuals and maintenance history for similar symptoms, inspection points, and safety notes.

The field technician compares that information with the actual equipment and shares the results. The headquarters specialist reviews both the field observations and the organization's maintenance knowledge to determine a safe course of action. AI does not independently approve or perform a hazardous operation.

After the work, AI organizes the symptoms, evidence, actions, and results. Once reviewed by a person, the record can be saved to the wiki or maintenance history, while required parts and follow-up inspections can be registered through permission-aware work tools.

1. Channels and direct messages

Teams can use topic-based channels, direct messages, and group conversations inside MiWork Agents.

• Public and private channels
• Colleague search and direct/group conversations
• Message reactions and threads
• Members and pinned messages
• AI mentions and invitations inside a conversation

People do not have to leave their collaboration context to use AI. They can bring AI into an existing discussion to summarize it or help with the next task.

2. AI conversations and work files

Private AI conversations remain a core part of the product.

• Answers grounded in accessible knowledge bases and attachments
• PDF, DOCX, HWP/HWPX, and image uploads
• Conversation-level documents and files
• Voice conversation entry
• Favorites and share links
• Text formatting and conversation history

Files used for personal work can be organized separately, while documents that should be searchable by a team can be connected to a knowledge base.

3. From conversations to a collaborative wiki

Useful discussions become difficult to find when they remain only in a message history. The MiWork Agents wiki turns channel discussions into documents that a team can continue editing.

Meeting notes, decisions, follow-up tasks, and references can be kept in one document. The wiki also supports document import/export and block-based editing.

Discuss in a channel
  ↓
Use AI to organize the discussion
  ↓
Publish it as a wiki document
  ↓
Let the team edit and reuse it

The goal is not only to generate an answer, but to leave behind knowledge the organization can use again.

4. Team knowledge bases

Knowledge bases manage documents and team-level read/edit permissions. Users can search only the knowledge they are allowed to access.

• Document upload and processing status
• Visibility for each knowledge base
• Team-level read and edit permissions
• Connected document management
• Source documents for grounded AI answers

This keeps AI search within the organization's existing responsibility and access boundaries.

5. Shared chatbots

Purpose-specific shared chatbots can handle repeated questions from teams or external visitors. Each chatbot has its own knowledge bases, visibility, and web-search policy.

Shared chatbots run separately from a user's personal AI environment. Personal memory and personal MCP connections are not exposed automatically; only the knowledge and capabilities explicitly assigned to the chatbot are used.

This supports internal help desks, policy search, product information, and customer-facing FAQ experiences.

6. Work-tool connections through MCP

MiWork Agents supports MCP so AI can connect to real business tools instead of stopping at a text response.

Administrators register organization-wide MCP connections, while users authenticate with their own accounts when required. AI can then query information or perform allowed work through those tools.

The important part is not the number of integrations, but controlling who can use which tool and under what authority.

7. Enterprise operations

An AI collaboration platform needs more than a chat interface. MiWork Agents includes an operations console for managing organizational use.

• User accounts and platform roles
• Teams and team membership roles
• Individual, team, and organization budgets
• Chat and embedding models with fallback order
• Shared chatbot creation, approval, and operations
• Policies and access scope
• MCP connection status
• Audit logs for authentication, permissions, and content changes

Administrators can monitor cost and usage, configure models and budget policies, and review important changes through audit records.

8. The same collaboration flow on desktop and mobile

The primary collaboration surfaces are also designed for mobile use. Channels, conversations, AI, and the wiki remain accessible through bottom navigation, while conversation lists and composer areas fit smaller screens without horizontal scrolling.

The aim is to keep the same work context when reviewing a document in the field or continuing a conversation while moving.

One connected workflow

The expanded product can be summarized as one collaboration flow.

People discuss work in a channel
  ↓
AI joins the conversation and organizes it
  ↓
Knowledge bases provide grounded sources
  ↓
Decisions and follow-up work move into the wiki
  ↓
Connected tools carry out permitted actions through MCP
  ↓
Administrators manage access, budgets, models, and audit records

Closing

With this upgrade, MiWork Agents has become an AI collaboration workspace where people and AI work together.

It is not intended to replace human collaboration with an AI conversation. It brings AI into the environment where people already communicate, find knowledge, create documents, and use work tools.

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