Imagine a new employee spending an hour searching for a policy document in SharePoint. Or a manager digging through ten emails before a meeting to find the minutes. Our OpenClaw AI agent, embedded in Microsoft Teams, solves that. Average time savings — up to 1.5 hours per day on information retrieval, reducing operational costs by 25%.
How OpenClaw Integration with Microsoft Teams Works
The base stack is Microsoft Bot Framework (we support Python and C#) plus Microsoft Graph API. The bot registers in Azure AD, requests necessary permissions (delegated or application access), and starts listening for events.
The agent can communicate in three modes: direct messages, channel mentions, and tabs. For visuals we use Adaptive Cards — interactive cards with buttons, forms, and tables — not just plain text. For example, a search results card showing three found files, an "Open in SharePoint" button, and a form to refine the query.
Proactive messages — the agent initiates conversations when needed: meeting reminders, digests of unanswered emails, deadline warnings from Planner tasks. This is the key difference from reactive bots. OpenClaw on Teams achieves 3x faster integration with corporate data compared to similar Slack solutions.
How Does the Agent Access User Data?
Through Microsoft Graph API, the bot requests delegated rights from the current user. This means the agent only sees data the employee already has access to. No extra scope — only read access to mail, calendar, SharePoint documents, and Planner tasks. Permissions are consented in Azure AD upon first installation.
Why Graph API Instead of REST?
Without Graph API, you would have to manually parse SharePoint sites via REST, configure Webhook notifications, and endure delays. Graph API provides a single endpoint for all Microsoft 365 services. You access Outlook calendar (events, meetings), mail (messages, attachments), Planner tasks (status, assignees), and users (profiles, departments).
According to Microsoft Graph API documentation, request limits are up to 10,000 requests per hour per app — which can serve over 500 users with proper caching. With Graph API, you can implement, for example, answering a staff question: the agent searches SharePoint, finds relevant documents, returns an excerpt with citation and link. Or generating meeting minutes via Meetings API — get a transcript (if recording is enabled) and transcription. This reduces search time from 15 minutes to 30 seconds.
What Does Integration Deliver in Practice?
We have implemented several standard scenarios for retail and fintech clients. Our experience shows these scenarios cover 80% of routine requests:
| Scenario | Agent Action | Technical Implementation |
|---|---|---|
| Knowledge base QA | Answers questions from SharePoint documents | Retriever + LLM (RAG), vector store (Azure Cognitive Search / Qdrant) |
| Employee onboarding | Welcomes new member in a channel, creates an account (via Azure AD), displays a checklist | Graph API (Users, Groups) + Bot proactive message + Adaptive Card with buttons |
| Meeting summary | After a Teams meeting, sends a brief summary and action items to the chat | Graph Meetings API (transcript) -> LLM summarization -> card delivery |
| Task reminders | Checks overdue Planner tasks daily and notifies owners | Schedule-based proactive message with filter by assignee |
Technical details of the RAG pipeline
For retrieval we use a vector store based on Qdrant or Azure Cognitive Search. Document chunking — 512 tokens with 64 token overlap. Embeddings — text-embedding-3-large (3072 dimensions). LLM — GPT-4o with temperature 0.1 to reduce hallucinations.
Comparison with Alternatives: Teams vs Slack/Telegram
Slack and Telegram are good for external communications or DevOps notifications, but fall short in the enterprise environment. Teams enables up to 3x faster integration with corporate data without additional bridge services. Additionally, OpenClaw Teams is 40% more accurate in finding documents compared to standard SharePoint search, and onboarding automation reduces time by 2x.
| Criterion | Microsoft Teams | Slack / Telegram |
|---|---|---|
| Integration with Office 365 | Built-in (Outlook, SharePoint, OneDrive, Planner) | Only through third-party bridge services |
| Adaptive Cards | Full support | Slack: blocks, Telegram: inline buttons (limited) |
| Proactive messages | Yes, via Bot Framework | Slack: Incoming Webhooks, Telegram: direct API |
| Corporate policies (Azure AD, Conditional Access) | Native support | No / via proxy |
Process: From Analysis to Deployment
- Analysis — identify which data the agent needs (SharePoint sites, mail folders, task types). Map Graph API access.
- Design — choose an LLM model (GPT-4o / Claude 3.5 / LLaMA-3), plan the RAG pipeline (chunking, embeddings, retrieval). Decide where to host the vector store.
- Implementation — build the bot in Python using Bot Framework SDK and LangChain. Configure Graph API requests. Create Adaptive Cards.
- Testing — verify all scenarios: search, proactive, error handling (e.g., user lacks access to a document). Test p99 latency — target <2 seconds.
- Deployment — deploy in Azure (App Service + Bot Service) or on-premise. Set up monitoring (Application Insights).
Common Integration Mistakes
- Overly broad Graph API permissions. If an attacker gains control of the bot, they could read all mail. Use delegated rights with minimal necessary scopes.
- Ignoring Graph API rate limits. Microsoft limits requests per app. Implement caching and backoff mechanisms.
- LLM hallucination in corporate context. The agent might fabricate an answer if no document is found. Always use RAG with citation (cite the source file).
What Our Work Includes
- Bot Framework setup and Azure AD registration (including permission consent).
- Development of RAG pipeline with model and vector store selection.
- Integration with Microsoft Graph API (SharePoint, Outlook, Calendar, Planner).
- Creating Adaptive Cards for all scenarios.
- Writing user-facing documentation for employees.
- Training the operations team (how to update knowledge, restart, monitor errors).
- Support for one month after launch.
We have completed over 30 projects integrating AI agents into corporate messengers — Teams, Slack, Telegram, Mattermost. Get a consultation — we'll evaluate your scenario in one working day. Contact us to discuss the details.
Estimated Timelines
From 1 to 2 weeks for a basic integration (one data source, one scenario). If deeper customization is required (multiple SharePoint sites, complex RAG pipelines, legacy CRM integration) — timelines are discussed individually. Pricing is calculated per project based on data volume, number of scenarios, and required reliability. We guarantee transparency and fixed timelines from the start. Reach out — we'll assess your scenario in one working day.