Building a Personal AI Chief of Staff on Telegram: 7 Problems and Stack Details

A developer on r/ClaudeAI has built a personal AI Chief of Staff on Telegram using Claude Sonnet as the brain. It integrates with Notion, Google Calendar, Gmail, a meeting transcription tool, a customer support platform, and Google Chat. The stack includes a Python Telegram bot, file-based context system with markdown files per project, and skills defined as markdown spec files for use cases like morning briefing and meeting processing.

What Actually Works

• Natural conversation with full tool access — agent decides which tools to call
• Meeting processing: drop a transcript link, agent extracts decisions, action items, saves structured brief
• Morning briefing on demand: tasks, calendar, open support tickets, suggested focus
• Drafting messages for any channel with the right tone
• Creating and updating tasks with natural language

7 Unsolved Problems

1. No memory between sessions: History is in-memory; bot restarts cause full amnesia. Considering a hot_context.md written at session end with TTL.
2. Purely reactive: Only responds when messaged. Wants proactive morning briefings at 9am and alerts for neglected clients. Struggle with format that makes user read it vs. dismiss as noise.
3. Can't tell if avoiding something vs. actually blocked: User procrastinates differently by task type. Wants agent to detect patterns and call out avoidance without becoming a nag.
4. No closure ritual: Tasks grow forever. Needs weekly "kill or commit" where things open >7 days get a date or get deleted.
5. Context loading blind spots: Project files work when client is mentioned explicitly, but fails on broad queries like "what should I focus on this week?" — agent doesn't check neglected relationships proactively.
6. Hosting kills file sync: Local bot dies when laptop closes. Moving to VPS means context files live on server. Unsure if git is the right sync layer.
7. Context files go stale: Agent appends logs but doesn't maintain top-level summary. After two months, sections are half-accurate. Questioning agent discipline, user discipline, or periodic jobs.

The original post asks for honest input from others who have built similar systems. The community likely has patterns for cross-session memory, proactive scheduling, and context maintenance.