Build Everything with AI Agents: Here's How

AI agents built with n8n can be deployed end-to-end—starting from a chat trigger, routing different input types, transcribing voice with OpenAI, and then using an LLM with tool access to take real actions like summarizing Gmail and creating Google Calendar events. The practical takeaway is that the “agent” becomes useful only after wiring tools (Gmail get/send, Calendar read/create) and testing each step until the data formats and prompts behave correctly.

The build starts in n8n with a new workflow and a trigger. A Telegram trigger is used first, because it’s a common entry point for automation. After connecting a Telegram account via an access token, the workflow is tested by sending a “hello world” message and confirming the incoming text appears in n8n’s output. Next comes a switch block that separates text messages from voice messages by checking which fields exist (text vs audio file ID). For voice, the workflow uses Telegram’s “get file” to retrieve the audio file ID, then passes that audio into OpenAI’s “transcribe a recording” step. A key debugging lesson appears immediately: silent or misconfigured audio leads to failed/incorrect transcriptions, so each step must be tested with real inputs.

Once both text and voice inputs produce usable text, the workflow connects to n8n’s “AI agent” module. The agent is configured with an Anthropic chat model (the transcript notes Sonnet, with a warning to select the newer Sonnet variant). The agent is also given a system prompt that instructs behavior (initially tested with “respond in full caps”). To make the agent do more than chat, the workflow adds tools and routes the agent’s output back to Telegram via “send a text message.” In testing mode, responses may not appear until the workflow is saved and activated, so the workflow must be toggled correctly during development.

The agent’s first major tool is Gmail. A “get many” Gmail tool is configured to fetch unread inbox messages (with a limit to control token cost). The system prompt is updated so the agent knows how to use the Gmail tool. Tests show the agent can summarize unread emails and select the relevant items from a larger set.

Next, Gmail “send” is added so the agent can draft and send emails. The transcript highlights a formatting pitfall: using the same placeholder expression for all fields caused an “invalid email address” error. Fixing it required giving each parameter a clear description (recipient email, subject, and body) so the LLM fills them correctly. After correcting the parameter descriptions and expressions, the agent successfully sends a properly formatted email.

Finally, Google Calendar is added with two tools: “get many” to read events for a time window, and “create” to schedule new events. Calendar creation initially fails due to date/time interpretation—specifically, the agent produced dates in the wrong year—so the system prompt must explicitly anchor “today” and enforce the correct date-time format. After adjusting the prompt and re-testing, the agent creates events with the intended start/end times.

By the end, the workflow is activated so it runs reliably from Telegram. The broader message is that n8n’s value comes from tool-augmented agents plus disciplined testing: every tool addition and prompt change requires step-by-step verification, because small data-format mistakes can break downstream actions. The result is an automation that can run indefinitely—summarizing emails and managing calendar tasks—without writing code.