Build and Deploy Agents to Production

​We have been building AI Agents in production for over an year.

If you want to learn too, we have put together a simple hands-on tutorial for you.

We build and deploy a Coding Agent that can scrape docs, write production-ready code, solve issues, and raise PRs, directly from Slack.

Tech Stack:

• Claude Code for code generation
• ​xpander​ as the Agent backend
• ​Firecrawl​ for web scraping

For context...

​xpander​ is a plug-and-play Backend for agents that manages scale, memory, tools, multi-user states, events, guardrails, and more.

Once we deploy an Agent, it also provides various triggering options like MCP, Webhook, SDK, Chat, etc.

Moreover, you can also integrate any Agent directly into Slack with no manual OAuth setups, no code, and no infra headaches.

One good thing is that this Slack Agent natively supports messages with audio, images, PDFs, etc.

Note: To learn how to build Agentic systems, we published a full course on Agents course (with implementation). Here’s what we have done in the crash course (with implementation):

• ​​​​In Part 1​​​​, we covered the fundamentals of Agentic systems, understanding how AI agents act autonomously to perform tasks.
• ​​​​In Part 2​​​​, we extended Agent capabilities by integrating custom tools, using structured outputs, and we also built modular Crews.
• ​​​​In Part 3​​​​, we focused on Flows, learning about state management, flow control, and integrating a Crew into a Flow.
• ​​​​In Part 4​​​​, we extended these concepts into real-world multi-agent, multi-crew Flow projects.
• In ​​​​Part 5​​​​ and ​​​​Part 6​​​​, we moved into advanced techniques that make AI agents more robust, dynamic, and adaptable, like Guardrails, Async execution, Callbacks, Human-in-the-loop, Multimodal Agents, and more.
• In ​​​​Part 7​​​​, we covered Knowledge of agentic Systems.
• In ​​​​Part 8​​​​ and ​​​​Part 9​​​​, we primarily focused on 5 types of Memory for AI agents, which help agents “remember” and utilize past information.
• In ​​​​Part 10​​​​, we implemented the ReAct pattern from scratch.
• In ​​​​Part 11​​​​, we implemented the Planning pattern from scratch.
• In ​​​​Part 12​​​​, we implemented the Multi-agent pattern from scratch.
• In ​Part 13​ and ​Part 14​, we covered 10 practical steps to improve Agentic systems.
• And in ​​​​​Part 15​​​​​, ​​​​​Part 16​​​​​ and ​​​​​Part 17​​​​​, we covered practical ways to optimize the Agent’s memory in production use cases.

Of course, if you have never worked with LLMs, that’s okay. We cover everything in a practical and beginner-friendly way.

Thanks for reading!