5 Common Mistakes When Scaling AI Agents
And How to Avoid Them
Just before we start today, a quick note:
I’m working on a new project about the future of AI agents, launching on May 20th!
If your company builds technology that supports developers - platforms for agent creation, deployment, monitoring, evaluation, orchestration, security, optimization, scaling, memory management, or observability (not just specific-purpose agents!) - this could be a great fit.
There’s a limited window to join.
To be included in the launch, everything must be finalized by May 13th.
If it sounds relevant, feel free to reach out:
👉 DM me on LinkedIn: https://www.linkedin.com/in/nir-diamant-ai
Okay, let’s begin: Imagine building a small garden shed. You plan it out, gather some wood and nails, and within a weekend, you've got a functional little structure. Now, imagine scaling that up to build a skyscraper using the same approach. Sounds ridiculous, right?
This is exactly what happens when developers try to scale AI agents from cool prototypes to production systems. What works for a demo with a handful of users often falls apart when real-world demands come knocking.
Let's explore the five most common pitfalls when scaling AI agents and how you can navigate around them.
1. The "One-Big-Brain" Bottleneck
The Problem: When starting out, it's tempting to build one giant AI agent that handles everything - planning, memory, tool usage, user interaction - all in one package. It's simple and works great for demos!
But as your system grows, this monolithic approach turns into a major headache. It's like having one employee handling customer service, accounting, shipping, and inventory all at once. Eventually, they become the bottleneck for your entire business.
Real-Life Example: Imagine a help desk with one person answering phones. When the call volume is low, everything runs smoothly. But what happens during a major outage when hundreds of customers call at once? Complete gridlock.
The Solution: Break your agent into specialized modules or "micro-agents" with specific responsibilities:
A "planner" agent that decides what needs to be done
An "executor" agent that carries out actions
A "memory" module that stores important information
This modular approach prevents one slow part from dragging everything else down. It's like having a team of specialists instead of a one-person band. Each component can be scaled independently based on demand.
2. Memory Mismanagement
The Problem: AI agents have limited "working memory" (their context window). You'll face one of two problems as you scale:
Your agent forgets important information as conversations grow longer
You try to fix this by cramming more context into each prompt, making your agent painfully slow and expensive to run
Real-Life Example: Think about human memory. If I asked you to memorize a 50-page manual and then answer specific questions about it, you'd struggle. You wouldn't try to keep the entire thing in your head - you'd refer back to relevant sections as needed.
The Solution: Implement smarter memory management:
Use retrieval over raw dumping: Instead of feeding everything into the prompt, store information in a database and fetch only what's relevant for the current query
Summarize conversations: After every few turns, summarize older parts of the conversation to keep the essential points without the verbosity
Separate short-term from long-term memory: Recent messages might be kept exactly as they were, while older information gets compressed or stored for retrieval when needed
Think of it like organizing your workspace: keep what you're currently working on within arm's reach, and file away everything else in labeled folders that you can access when needed.
3. Multi-Agent Coordination Chaos
The Problem: You might think: "If one agent is good, ten must be better!" But without proper coordination, multiple agents can create more problems than they solve. They might duplicate work, contradict each other, or get stuck in endless discussion loops.
Real-Life Example: Imagine a kitchen with ten chefs but no head chef. Without someone coordinating who makes what, you'd have seven people making dessert, nobody cooking the main course, and three people arguing about how to chop onions.
The Solution: If you're using multiple agents, you need a clear structure:
Define clear roles and protocols for how agents interact
Establish shared knowledge that all agents can access and update
Limit unnecessary communication between agents
Create a coordination system (like a supervisor agent or a task queue)
The key is to make sure your agent team functions like a well-rehearsed orchestra rather than a chaotic jam session.
4. The Runaway AI Bill
The Problem: AI usage costs can skyrocket faster than you expect. Each API call, each token processed, and each model inference adds up. Without cost monitoring, you might be burning cash without realizing it.
Real-Life Example: It's like leaving all the lights and appliances running in your house 24/7. You don't notice the waste until the utility bill arrives - and by then, you've already spent the money.
The Solution: Make cost management a priority from day one:
Track and monitor token usage for every request
Optimize prompts to be concise (every token counts!)
Use smaller, cheaper models for simple tasks, saving the powerful ones for complex problems
Limit unnecessary iterations or steps in your agent workflows
Set budgets and alerts to catch runaway processes
This isn't about being cheap - it's about being efficient. Even tech giants with deep pockets optimize their AI costs. For startups and smaller companies, this can be the difference between success and failure.
5. Overengineering the Agent
The Problem: Once you discover the power of AI, it's tempting to use it for everything. Why write simple code when you can ask an AI to do it? This leads to unnecessarily complex systems that are slow, expensive, and prone to unexpected failures.
Real-Life Example: It's like using a bulldozer to plant a flower. Yes, the bulldozer is powerful and can move dirt, but it's massive overkill and might crush what you're trying to create.
The Solution: Use AI for what it's good at, and traditional programming for everything else:
Let deterministic code handle tasks with clear rules (sorting, filtering, calculations)
Save AI for tasks requiring creativity, language understanding, or complex reasoning
Simplify your agent's workflow by removing unnecessary steps
Regularly review your system to see if any AI components could be replaced with simpler, more reliable code
Remember: the goal isn't to maximize AI usage but to solve problems efficiently.
Bringing It All Together
Scaling AI agents from prototype to production is a journey that requires both AI knowledge and solid engineering principles. Think of your AI agent as a tool in your toolbox - powerful but meant to be used appropriately, not for everything.
The most successful AI systems often have a lot of traditional software engineering under the hood, with AI strategically applied where it adds the most value. They're built with an eye toward:
Modularity: Breaking complex problems into manageable pieces
Efficiency: Using resources wisely and monitoring costs
Reliability: Making the system robust against unexpected inputs
Simplicity: Avoiding unnecessary complexity
By avoiding these five common mistakes, you can build AI agents that don't just work well in demos but thrive under the demands of real-world use.
The goal isn't to build the fanciest AI system possible - it's to create something that solves real problems reliably, efficiently, and at scale. Keep that north star in mind, and you'll be ahead of 90% of AI projects out there.