If you've ever built an AI agent that works beautifully on your laptop, only to feel a quiet sense of dread when someone says "let's put this in production on AWS," you are not alone.
Here’s how the story goes: the agent logic comes together quickly. The demo looks great. Then the moment production enters the conversation, the good vibes are gone.
Suddenly it is not about prompts or models anymore. It is about infrastructure, security, observability, and cost. All at the same time. Not cool.
What makes this especially frustrating is that nothing is technically "wrong."
The tools work. AWS is powerful. The models are impressive. But AI agents are not normal web apps. One user request can trigger multiple model calls, tool invocations, vector searches, retries, and external APIs. Latency compounds. Costs become harder to predict. Debugging stops being deterministic and starts feeling probabilistic.
The gap between "it works on my machine" and "this will survive real users" is where many developers get stuck.
The 4 Ways Developers Try to Bridge This Gap
When developers hit this wall, they typically reach for one of four approaches. Each has real trade-offs, and understanding them helps explain why shipping agents feels harder than it should be.
** Approach 1: AWS Console (Click-Ops)
The AWS Console is where most developers start. You log in, click through wizards, and configure services manually.
What you get:
• Visual interface for learning AWS concepts
• Quick experiments without writing code
• Immediate feedback on configuration changes
The reality:
• 79+ manual steps just to deploy a basic Bedrock Agent
• Navigate 4+ service consoles (Bedrock, Lambda, IAM, CloudWatch)
• Configure IAM roles and policies by hand
• Debug cryptic permission errors with no clear path
• No version control or reproducibility
• RAG systems require 127+ configurations across 6 services
• Hours to days of setup time
The console is fine for learning, but you'll quickly hit limits. There's no version control, no way to review changes, and no easy path to reproduce what you built. Most teams graduate to something else within weeks—or they stall here indefinitely.
*Approach 2: Infrastructure-as-Code (Terraform/CDK)
*
Terraform and AWS CDK let you define infrastructure in code. You write HCL or TypeScript, and the tool provisions AWS resources.
What you get:
• Version control for infrastructure changes
• Reproducible deployments across environments
• Multi-cloud support (Terraform) or AWS-native integration (CDK)
• Team collaboration through code review
• Maximum flexibility and control
The reality:
• Steep learning curve for AWS service configuration
• Separate codebase from your application logic
• Still need to understand IAM, VPCs, security groups
• Debugging means understanding both your code and AWS
• Days to weeks of initial setup
• Ongoing maintenance as AWS services evolve
Terraform and CDK give you maximum flexibility, but you're maintaining two codebases: your application and your infrastructure. This is not a skills problem—it's that AI agents introduce multi-step behavior on top of already distributed systems. Capacity planning becomes guesswork. Debugging becomes non-linear. Cost control becomes something you worry about after the fact.
*Approach 3: Framework-Specific Deployment
*
Frameworks like LangChain, LlamaIndex, Strands, and Vercel AI SDK provide libraries for building AI applications, with varying levels of deployment support.
What you get:
• Familiar development patterns
• Rich ecosystem of integrations
• Good local development experience
• Community support and examples
The reality:
• Still requires separate infrastructure setup
• Limited production-ready deployment patterns
• No built-in cost tracking or observability
• Manual security and IAM configuration
• Framework lock-in without infrastructure portability
These are excellent libraries for writing AI code, but they don't solve the deployment problem. Think of it as the difference between buying lumber (SDK) vs. moving into a furnished house (full-stack deployment).
*Approach 4: Production-Ready Prototypes (LEAP Stacks)
*
After seeing this pattern over and over in workshops, conferences, and community chats around the world, I decided to try a different approach, which eventually became LEAP Stacks.
Instead of just recording video content or starting from documentation, frameworks, or CLI scaffolding, the idea was to start with working, opinionated AI systems deployed directly into a developer's own AWS account with video guides included. Safely and temporarily.
What you get:
• Single CloudFormation deployment (< 7 minutes)
• Pre-configured IAM roles and security policies
• 12 production-ready stacks: chatbots, RAG systems, autonomous agents, voice AI
• Real-time cost tracking per message
• Auto-cleanup after 2 hours (configurable) to prevent surprise bills
• Built-in observability (CloudWatch logs, DynamoDB tracking)
• GitHub export to generate full CDK repository
• Live code editing via dashboard
• Support for all AWS Bedrock models (Claude, Nova, Llama)
Example stacks:
• RAG Knowledge Base (OpenSearch): Chat with documents using vector search
• Agent with MCP Tools: Serverless AI agent with Model Context Protocol
• Voice AI Agent: Real-time voice assistant powered by Nova Sonic 2
• Autonomous Agent Runtime: Self-updating agent with persistent memory
• Agentic Automation (n8n): Visual workflow automation in your VPC
The reality:
• Less granular control than raw Terraform (by design)
• Focused on prototyping and learning (though exportable to production)
• AWS-only (no multi-cloud support)
• Newer ecosystem than Terraform
The goal was never to hide AWS or replace frameworks. It was to remove the hardest part of getting started: figuring out where to begin, how the pieces securely fit together, and what actually matters so you can focus on unlocking value from the AI agents.
What "Production-Grade" Actually Means for AI Agents
When people say they want to take an agent to production, they rarely mean "make the demo public." Production-grade usually implies a few unglamorous but essential things:
The system needs to be gated and handle multiple users and long-running workflows without quietly falling apart.
You need tracing and logs that let you understand why an agent behaved the way it did across model calls, tools, and orchestration layers.
Security needs to be boring and correct, with least-privilege access to models, data, and tools.
You need reproducibility so you can roll changes forward and backward without fear. And you need cost visibility that tells you what a single conversation actually costs, not just what a service costs per hour.
Why Deploying Agents Feels Harder Than "Normal" Web Apps
Traditional web apps are mostly deterministic. A request comes in, some logic runs, a response goes out. When something breaks, there's usually a log that points to the problem.
AI agents are different. Each request can branch. A model might call a tool. The tool might fail. The model might retry. Context grows. Latency sneaks in from places you didn't expect. Failures compound instead of failing fast. When something goes wrong, the question is rarely "what line of code broke" and more often "which step in this chain behaved differently this time."
On AWS, that complexity often sprawls across services. Without a strong opinionated deployment pattern, developers can spend most of their time wiring infrastructure instead of shaping agent behavior. This is not a skills problem!
A prototype is not a demo. It is an adapted system under real constraints.
Stop Digging, Start Shipping
AI agents are not failing because of weak models; they're stalling because the path to production is currently a mountain of "infrastructure archaeology." I believe we should be spending our energy on shaping agent behavior and outcomes, not on wiring together IAM roles and VPCs just to see if a prototype is viable.
If you're ready to stop building demos that you're afraid to deploy and start building systems that are production-grade by default, I'd love for you to:
🚀 Deploy LEAP Stacks 2 on GitHub — It’s free and open source, installs via CloudFormation, 12 production-ready stacks built with love and ready to deploy:
https://github.com/bfateen/leapstacks2
I took about 8 months to build this latest version and I'm ridiculously excited to hear your feedback after you give it a spin.
Happy building! :)
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