Day 11: How to deploy AI Agents?

Day 11: Deploying AI Agents

👋 Welcome to Lesson 11 of the 14-Day Course. Last lesson, we talked about why software engineering fundamentals matter. Today, we're bridging the gap between building something that works on your machine… and something that actually runs in the real world.

Today we will cover:

• 4 most common deployment patterns

• Why it matters for your career in AI

• Common challenges with Agents

• Tools we recommend and use

• How to deploy with Vercel

• More resources on Agents with links

📌 Missed a lesson? Catch up here:

 👉 Day 1: Intro to Agents 
👉 Day 2: Agents vs Workflows 
👉 Day 3: RAG and Tool Use 
👉 Day 4: Memory 
👉 Day 5: Evals for Agents 
👉 Day 6: Context for Agents 
👉 Day 7: Multi-Agents (MAS) 
👉 Day 8: Setup Git and Github 
👉 Day 9: AI assisted coding 
👉 Day 10: SWE fundamentals 
 

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Join next AI Engineering Bootcamp which starts on June 12th!
Only 5 spots left with TIER2 ($300 OFF discount)!

1. What are 4 most common ways to deploy agents? 🤔

When we talk about deploying agents, we mean packaging them in a way that allows others to use, access, or automate them on the cloud, rather than on your local machine.

Here are 4 most common deployment patterns for AI Agents:

• Web apps: Wrap your agent in a React or Next.js frontend and deploy to Vercel.

• APIs: Build a FastAPI app around your agent logic and deploy to Render, Railway or FastAPI Cloud.

• Serverless functions: Ideal for stateless agents that run in response to triggers (e.g., via Vercel serverless, AWS Lambda)

• Long-running workers: For stateful agents that continuously act, monitor or orchestrate workflows (deployed typically with Docker containers, VMs, Kubernetes)

2. Why is deployment important? 👍

If you want to break into AI, especially into technical roles, like Agent Engineer, LLM developer, or Product Engineer, knowing how to deploy agents is non-negotiable.

Here’s why deploying your agent early gives you an edge:

✅ You can collect real-world data early on.
So you can iterate, test, and improve incrementally.

✅ You will gain full-stack thinking. So you can connect the dots between LLM logic, backend infrastructure, and UX.

✅ You will make your portfolio stand out. Anyone can show notebooks, but few can share the deployed version.

Deployment is a significant aspect of the job, and it's why many employers are hesitant to hire graduates or even PhDs. Remembe, in interviews, demos, or hackathons, a deployed link always beats a GitHub repo.

To become industry-ready, join our next cohort before the start date, June 12th. Limited spots available.

3. Common Challenges ❤️‍🩹

You might think deploying AI agents is just like deploying any app. It's not. It has it’s unique challenges, like:

• System Integration: Connecting agents to diverse data sources and tools (like CRMs, ERPs, databases, legacy apps) is challenging due to varying APIs, data formats, authentication methods, and real-time requirements.

• State management: Many agents require persistent memory, chat history, or task state. You’ll often need to integrate a vector store, database, or session layer.

• Latency: Most LLM calls aren’t instant. Without async handling or streaming outputs, things will be slower.

And many more edge-cases that you will only discover after deployment.

4. Tools we recommend 🛠️

Want to deploy your AI agent today?
Check out these deployment options here:

Before you write a line of code, start simple, like with single-action agents, and expand complexity as you need to.

5. How to deploy with Vercel?

At the Agent Engineering Bootcamp, we teach you how to deploy agents using platforms like Vercel.

Here is how it works:

1. Connect your GitHub repo to Vercel

2. Every time you git push, Vercel:

   • Runs build commands

   • Deploys your code

   • Gives you a live URL to test + share

3. You can preview changes before going live.

   1. Zero DevOps Required. (which is why we recommend it) Vercel handles servers, scaling, SSL, and builds for you

Just push to GitHub, and your app is live within seconds.

🎉 Congratulations!

You finished Day 11! Tomorrow, we’ll dive into API and Web communication fundamentals for Agents!

If you missed yesterday’s live session on Agent Engineering 101 fundamentals, go and watch it. You will find an example project with OpenAI Agent SDK + MCP.

All the lessons in this course are based on first-hand experience building and shipping real agentic workflows, from API tools to full-stack workflows. You won’t find this blend of practical depth and full-stack AI engineering insights anywhere else.

If you're serious about mastering agent deployment, system design, and full-stack AI development, join our 6-week Agent Engineering Bootcamp.

🚨 P.S. TIER1 ($500 off) is now sold out.
We're now on “TIER2” pricing: $300 off, with only 5 seats left before the next price increase.

Spots are going fast, secure yours here before the next tier kicks in.
🎟 Use code “TIER2” for $315 off

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