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  "body_markdown": "Oct 4, 2025\nLLMs\nBuild and Deploy Agents to Production\n...explained step-by-step.\nAvi Chawla\n👉\n​\nWe have been building AI Agents in production for over an year.\nIf you want to learn too, we have put together a simple hands-on tutorial for you.\n0:00\n/\n10:39\n1×\nWe build and deploy a Coding Agent that can scrape docs, write production-ready code, solve issues, and raise PRs, directly from Slack.\nTech Stack:\nClaude Code for code generation\n​\nxpander\n​\nas the Agent backend\n​\nFirecrawl\n​\nfor web scraping\nFor context...\n​\nxpander\n​\nis a plug-and-play Backend for agents that manages scale, memory, tools, multi-user states, events, guardrails, and more.\nOnce we deploy an Agent, it also provides various triggering options like MCP, Webhook, SDK, Chat, etc.\nMoreover, you can also integrate any Agent directly into Slack with no manual OAuth setups, no code, and no infra headaches.\nOne good thing is that this Slack Agent natively supports messages with audio, images, PDFs, etc.\nNote: 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):\n​\n​\n​\n​\nIn Part 1\n​\n​​​\n, we covered the fundamentals of Agentic systems, understanding how AI agents act autonomously to perform tasks.\n​\n​\n​\n​\nIn Part 2\n​\n​​​\n, we extended Agent capabilities by integrating custom tools, using structured outputs, and we also built modular Crews.\n​\n​\n​\n​\nIn Part 3\n​\n​\n​\n​\n, we focused on Flows, learning about state management, flow control, and integrating a Crew into a Flow.\n​\n​\n​\n​\nIn Part 4\n​\n​\n​\n​\n, we extended these concepts into real-world multi-agent, multi-crew Flow projects.\nIn\n​​​\n​\nPart 5\n​\n​\n​\n​\nand\n​​​\n​\nPart 6\n​\n​\n​\n​\n, 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.\nIn\n​​​\n​\nPart 7\n​\n​\n​\n​\n, we covered Knowledge of agentic Systems.\nIn\n​​​\n​\nPart 8\n​\n​\n​\n​\nand\n​​​\n​\nPart 9\n​\n​\n​\n​\n, we primarily focused on 5 types of Memory for AI agents, which help agents “remember” and utilize past information.\nIn\n​​​\n​\nPart 10\n​\n​\n​\n​\n, we implemented the ReAct pattern from scratch.\nIn\n​\n​\n​\n​\nPart 11\n​\n​​​\n, we implemented the Planning pattern from scratch.\nIn\n​\n​\n​\n​\nPart 12\n​\n​​​\n, we implemented the Multi-agent pattern from scratch.\nIn\n​\nPart 13\n​\nand\n​\nPart 14\n​\n, we covered 10 practical steps to improve Agentic systems.\nAnd in\n​\n​\n​\n​\n​\nPart 15\n​\n​\n​​​\n,\n​\n​\n​\n​\n​\nPart 16\n​\n​\n​​​\nand\n​\n​\n​\n​\n​\nPart 17\n​\n​\n​​​\n, we covered practical ways to optimize the Agent’s memory in production use cases.\nOf course, if you have never worked with LLMs, that’s okay. We cover everything in a practical and beginner-friendly way.\nThanks for reading!\nPublished on Oct 4, 2025\nComments\nShare\nCopy link\nShare to X\nShare to Facebook\nShare to Linkedin\nCopied\nPrevious\nModel Development and Optimization: Compression and Portability\nNext\nModel Deployment: Serialization, Containerization and API for Inference",
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