Product Name: Multi-Agent Slack Bot (OpsBot / AgentHub)
Version: 1.1
Date: April 08, 2026
Author: Grok – AI Agentic Engineer
Status: Revised & Consolidated
The Multi-Agent Slack Bot is an intelligent, extensible automation platform built inside Slack.
It features:
- A robust Dashboard Agent that automatically scrapes key metrics from a web-based dashboard and delivers formatted reports.
- An LLM-powered intent router that intelligently routes user requests to the appropriate specialized AI Agent.
- Support for N number of independent agents (easily extensible).
- Daily autonomous reporting every day at 6:00 PM IST.
- Thread-based conversations with short-term memory for natural follow-ups.
The goal is to eliminate manual dashboard checking, reduce operational toil, and turn Slack into a self-serve AI operations hub for the team.
- Deliver accurate dashboard metrics automatically every evening at 6 PM IST.
- Enable team members to request metrics and trigger agent actions using natural language.
- Create a modular platform where new specialized agents can be added quickly.
- Support contextual follow-up conversations within the same Slack thread.
- Significantly reduce time spent manually checking dashboards and compiling reports.
- Responds to
@botmentions and direct messages. - Maintains conversation context within Slack threads.
- Supports rich formatting using Slack Blocks and emojis.
- Ready for slash commands (
/dashboard,/run-agent, etc.).
- Uses LLM (OpenAI GPT-4o-mini or xAI Grok) to classify user intent.
- Dynamically routes requests to the correct agent based on agent name + description.
- Graceful fallback with helpful suggestions when intent is unclear.
- Low-temperature deterministic routing.
- On-demand mode: Triggered by user messages (e.g., "show dashboard metrics", "run daily report").
- Scheduled mode: Automatically runs every day at 6:00 PM IST and posts to a configured channel.
- Uses Playwright to navigate, login (if needed), and scrape metrics.
- Extracts configurable KPIs using CSS selectors.
- Formats and sends clean, readable reports to Slack.
- Includes optional screenshot capture for debugging.
- All agents inherit from a
BaseAgentabstract class. - Each agent implements a standard
run()method. - New agents can be added by creating a new class and registering it (no router changes needed).
- Agents operate independently with optional thread context.
- Short-term conversation memory per Slack thread.
- Supports follow-up questions (e.g., "compare with yesterday", "explain the drop").
- Designed for easy upgrade to persistent storage (Redis).
- Daily cron job at 18:00 IST (Asia/Kolkata timezone).
- Configurable target channel for autonomous reports.
- Background scheduler using APScheduler.
- What is the exact dashboard URL?
- Can screenshots or temporary access be provided to inspect page structure and define stable selectors?
- Does the dashboard offer a REST/GraphQL API (preferred over scraping)?
- List of exact metrics/KPIs to extract.
- Authentication method (username/password, SSO, MFA, VPN)?
- How frequently does the UI change? History of selector breakage?
- Should screenshots be attached with every report?
- Preferred LLM provider and model?
- Any cost, latency, or data privacy constraints?
- Should rule-based keyword fallback be added?
- What are the next 1–2 agents planned after Dashboard Agent?
- Is simple intent routing sufficient, or do we need full agentic reasoning (planning, ReAct, tool use)?
- Which channels should the bot operate in?
- Preferred interaction style (mentions only, DMs, slash commands)?
- Default channel for the daily 6 PM autonomous report?
- Preferred deployment target (Docker + VPS, Render, Railway, AWS Lambda, etc.)?
- Do we need failure alerts via Slack when scraping fails?
- Any specific security or secret management requirements?
- Performance: Bot response < 3s for routing, full scrape < 45s.
- Reliability: Robust error handling, timeouts, and retry logic.
- Security: Secrets managed via environment variables; no hard-coded credentials.
- Scalability: Support multiple agents; horizontal scaling possible.
- Observability: Detailed logging; optional monitoring and alerts.
- Maintainability: Clean, modular code structure.
- Language: Python 3.10+
- Slack Framework:
slack-bolt - Scraping: Playwright (Chromium)
- LLM: OpenAI or xAI Grok API
- Scheduling: APScheduler
- Others: python-dotenv, pydantic
- Optional: Redis (for persistent memory), Docker
- As a team member, I can ask "@Bot show me dashboard metrics" and receive the latest report quickly.
- As an operations lead, I automatically receive the daily 6 PM report in the designated channel.
- As a developer, I can add a new agent in under 10 minutes.
- As a user, I can ask follow-up questions in the same thread and get contextual responses.
- Daily report delivery success rate ≥ 99%
- Intent routing accuracy ≥ 95%
- Average response time < 5 seconds
- New agent addition time ≤ 10 minutes
- Significant reduction in manual dashboard checking time
Assumptions:
- Dashboard UI is reasonably stable.
- Necessary credentials and access can be provided securely.
- LLM routing will perform reliably with the defined agents.
Risks & Mitigations:
- Dashboard UI changes breaking selectors → Mitigated by screenshots and early alerts.
- Authentication failures → Robust login handling and failure notifications.
- LLM misrouting → Low temperature + fallback messages.
- High maintenance cost → Preference for API over scraping where possible.
- Gather answers to all questions in Section 4.
- Finalize dashboard selectors and authentication approach.
- Implement and test the Dashboard Agent first (highest value + highest risk).
- Build the routing layer and base framework.
- Add scheduling, memory, and deployment packaging.
- Conduct end-to-end testing and go-live.
Approval:
- Approved for Implementation