Build an AI Agent for Telegram, Slack, or Email — Without Code

What Is a GenMB AI Agent?

An AI Agent is not a chatbot widget on a website. It's an autonomous AI that lives directly on the messaging channels you and your users already use — Telegram, Slack, and Email. It responds to messages, remembers past conversations, can look up information from a knowledge base you provide, and can even send proactive scheduled messages on a cron schedule.

The difference from a traditional chatbot is meaningful. Traditional chatbots are rule-based — they follow decision trees and break the moment a user goes off-script. GenMB Agents are LLM-powered and context-aware. They understand intent, maintain memory across sessions, and can take actions via tools — not just generate text.

The Three Channels

Telegram is the fastest to set up. Create a bot via @BotFather, copy the token, paste it into your agent config, and you're live. Your agent responds to every direct message sent to the bot. It works in groups too, responding when mentioned.

Slack connects via OAuth. You specify which channel IDs the agent monitors and responds in. This makes it ideal for internal tools — a support bot in your #help channel, a product update bot in #announcements, or an ops bot that responds to incident commands.

Email connects a Gmail account. Users email your agent and it replies. You can optionally set a trigger email address — useful for forwarding-based setups where a shared inbox forwards to the agent. The agent replies as itself, not as the Gmail address, so your branding stays clean.

You can connect multiple channels to a single agent. Conversation history is tracked per participant per channel — a Telegram user and a Slack user interacting with the same agent have completely independent memories.

The Knowledge Base: RAG for Everyone

Every agent can have a knowledge base. Upload PDF, TXT, Markdown, or CSV files — product documentation, FAQs, policy documents, pricing sheets, whatever your agent needs to answer questions accurately.

Under the hood, GenMB chunks each file into overlapping segments and stores them in a vector database. When a user asks a question, the agent runs a semantic search across all chunks, retrieves the most relevant ones, and injects them into its context before generating a response. This is Retrieval-Augmented Generation (RAG), and it means your agent can answer specific questions about your product without hallucinating made-up answers.

Files up to 10MB are supported, with an upload rate of 30 files per hour. Adjust the similarity threshold and result count in the agent settings to tune how aggressively the knowledge base is consulted.

A concrete example: a SaaS company uploads their entire help documentation — 40+ articles as Markdown files. Their support agent on Telegram now answers "how do I export my data?" with the exact steps from the relevant help article, verbatim, rather than a generic guess.

Memory: The Agent Remembers You

The knowledge base is static — it's content you upload. Memory is dynamic — it's what the agent learns from conversations.

When memory is enabled, the agent automatically extracts and stores facts from interactions: user preferences, names, past topics discussed, decisions made. The next time that user messages the agent — even days later, in a new conversation — the agent recalls relevant memories via semantic search and personalizes its response accordingly.

Up to 100 memories per agent are stored, configurable. You can view and delete individual memories from the agent settings panel if needed.

This is the difference between "Welcome back! How can I help you?" and "Welcome back, Sarah. Last time you were working on integrating Stripe — did you get that sorted out?"

Built-in Skills

Four skills are available to every agent, each can be enabled or disabled:

• web_search — real-time web search results injected into the response. Use for questions about current events, competitor pricing, or anything outside your static knowledge base.
• http_request — the agent can call any URL during a conversation. Useful for fetching live data from your own API mid-conversation.
• current_time — date and time in any timezone. Surprisingly useful for scheduling-related conversations.
• weather — current conditions for any location.

Custom Tools: Connect to Your Own APIs

Beyond built-in skills, you can define up to 10 custom tools — HTTP endpoints the agent can call when it decides it needs live data to answer a question. Each tool has a name, a description (which the AI uses to decide when to invoke it), an HTTP method (GET or POST), a URL, optional headers, and a body template with {{variable}} interpolation.

Example: a tool named get_order_status with description "look up the status of a customer order by order ID" pointing to your /api/orders/{{order_id}}/status endpoint. When a customer says "where is my order #12345?", the agent automatically calls the tool with the order ID extracted from the message, and responds with the live status.

Escalation to Humans

Not every conversation should be handled by AI. Configure escalation keywords — phrases like "speak to a human", "talk to support", "this is urgent" — and when the agent detects them, it sends a configurable escalation message to the user and notifies a human via email or Slack, with the full conversation context included.

This creates a clean handoff pattern: the AI handles the 80% of conversations that are straightforward, and escalates the 20% that need human judgment. No users get stuck in an AI loop when they genuinely need a person.

Scheduled Messages

Agents aren't passive — they can initiate conversations on a schedule. Configure up to 5 scheduled tasks per agent, each with a cron expression, a target channel, and a message template. Set quiet hours to prevent messages outside business hours.

Real use cases:

• A community manager bot sends a daily "Thread of the Day" to a Slack channel every morning at 9am
• A customer success agent sends a weekly check-in to active trial users via email every Monday
• An ops bot posts a daily status summary to a Slack #ops channel each evening at 6pm

Try It

AI Agents are available on the Business plan. If you're building internal tools, customer support bots, community management agents, or anything that lives on a messaging channel, head to the Agents section and create your first agent. You'll have it deployed and responding to messages in under 10 minutes.