🔥Building an Enterprise Slack Bot with Agentic AI: A Complete AWS Architecture Guide🔥

aka, find outages in PagerDuty, identify changes in GitHub, find tickets in Jira, tie em all together

Hey all!

Today we’re going to explore something pretty exciting in the world of enterprise AI applications. I recently got my hands on an evolution of the Vera genAI solution that I’ve covered extensively before - and this new version represents a significant architectural leap forward.

If you’ve been following my previous posts about Vera, you’ll remember it was built with Python directly without many external libraries, focusing on raw AWS Bedrock integration. This new implementation takes that foundation and transforms it into a proper agentic AI system using the Strands framework. Instead of just being a conversational AI, it can now take actions and use tools across your entire enterprise toolchain.

What makes this implementation particularly interesting is how it solves those everyday enterprise pain points we all know too well. You know that juggling act between Slack, PagerDuty, GitHub, and Jira just to get context on a single issue? This bot brings all those systems together in one conversational interface, powered by AWS Bedrock with Claude Sonnet 4 as the backend.

Here’s where the agentic capabilities really shine. The bot uses Model Context Protocol (MCP) to actually connect to your existing platforms. When someone asks “what incidents are assigned to me?” it goes and queries PagerDuty directly through AWS Bedrock’s processing. When they want to know about pull request status, it hits the GitHub API. The AI becomes a conversational interface to your entire toolchain, all orchestrated through AWS Bedrock’s model capabilities.

The system includes AWS Bedrock Guardrails for content filtering and even supports retrieval-augmented generation through Bedrock Knowledge Bases. It’s engineered for enterprise use with proper secret management, brand voice guidelines, and all the infrastructure automation you’d expect in a production environment.

In this article, we’ll walk through the complete architecture, examine how the Strands agentic framework transforms the original Vera approach, and look at how AWS Bedrock powers the multi-service integrations. Whether you’re thinking about building your own AI assistant or just curious about modern agentic architectures, this implementation has some great lessons to offer.

Let’s jump in and see how this all fits together!

If you want to skip the write-up and just read the code, you can find that here:

github.com/KyMidd/SlackStrandsAgenticBot