If you’ve built agents using tutorials, worked with frameworks like LangChain, or deployed chatbots in production, you’ve likely encountered some challenges that weren’t covered in the getting-started guides. What seemed straightforward in demos becomes complex when handling real-world scenarios. What worked perfectly for simple tasks starts breaking down when you scale up.
This isn’t a failure on your part — it’s the nature of working with a rapidly evolving technology. The field of AI agents is advancing at an extraordinary pace, and many of the patterns and approaches that made sense even six months ago are no longer optimal for today’s capabilities.
There’s significantly more complexity involved in reliable agent systems than most resources discuss, and more importantly, it isn’t often where you’d expect.
This blog will help you understand the constraints and challenges that become apparent when you move from prototype to production-ready AI agents, regardless of which tools or frameworks you choose to use. Think of it as mapping the iceberg. Most of the complexity lies beneath the surface, invisible until you encounter it firsthand.
When building agent systems, there are several constraints that become increasingly important as you move from simple demos to production workloads. Many of these constraints are subtle — they don’t prevent your system from working initially, but they become bottlenecks as complexity increases.
Let’s walk through the key constraints you’re most likely to encounter, along with how they manifest in practice and why they matter for system design.
Everything that goes into an LLM must be converted to text or an image. Word docs, PDFs, database schemas, and API responses all become tokens in that context window. This may seem obvious, but the implications run deeper than most people realize.
The key insight is that how you convert something to text has an enormous impact on whether the model can work with it effectively. LLMs process information much like humans do — they rely on structure, formatting and visual hierarchy to understand relationships and meaning.
Consider a simple example: a financial table with rows and columns. If you serialize this as comma-separated values (CSV), you lose the visual structure that makes relationships clear. The model has to work much harder to understand which numbers relate to which categories. But if you preserve the table structure using markdown formatting or maintain clear visual separation, the model can process it much more effectively.
This principle extends to everything from PDF extraction (where layout preservation can be critical) to API response formatting (where consistent structure helps the model parse information reliably). Spending time on thoughtful text representation often has a bigger impact on agent performance than tweaking prompts or adding more sophisticated reasoning chains.
Most people understand that LLMs have context windows. And most people interpret those context windows as simple size limits. But there’s a subtler constraint that’s often overlooked: attention limits. The context window isn’t simply storage. It’s more like human working memory, where everything competes for attention. This manifests in several ways:
We find that agents often perform better with carefully curated, relevant information than with comprehensive but unfocused context. The challenge is determining what information is truly necessary for each specific task, rather than including everything “just in case.”
Here’s a constraint that often catches people by surprise: every tool call leaves a permanent record in the conversation history. A tool is a function that an AI agent can use to perform specific actions (like searching or calculating), and a tool call is when the agent actually executes one of these tools. When an agent calls a tool, both the call and its result become part of the context that gets sent with every subsequent API request.
This creates some interesting dynamics:
This constraint highlights the importance of designing tools that are not only functional but also context-efficient. Tools that return concise, relevant information help maintain agent performance throughout longer sessions.
When you write instructions for an agent, those instructions become part of a larger hierarchy of directives that all compete for the model’s attention:
Your specific agent instructions are only one layer in this stack. The more layers there are, and the more verbose each layer is, the less attention your specific guidance receives.
This constraint becomes particularly apparent when working with many tools. Each tool requires a description that explains what it does and how to use it. An agent with 20 tools might have more tokens dedicated to tool descriptions than to actual task instructions.
Successful agent design often involves finding the right balance between comprehensive instructions and attention efficiency. Sometimes the most effective approach is to provide minimal, focused guidance rather than exhaustive detail.
There’s a constraint that’s become apparent only recently: many traditional “prompt engineering” techniques are no longer optimal with modern models. This creates a challenge for practitioners who learned effective patterns with earlier generations of LLMs.
Techniques like explicit chain-of-thought prompting, step-by-step reasoning instructions, and “think before you answer” patterns were developed as workarounds for models that had limited reasoning capabilities. These approaches helped earlier models produce more reliable outputs by forcing them through structured thinking processes.
However, newer models with built-in reasoning capabilities often perform better when given more direct, task-focused instructions. The explicit reasoning scaffolding that helped GPT-3.5 can actually limit the more sophisticated reasoning patterns that models like Claude 3.5 or GPT-4 naturally employ.
This creates an interesting challenge: the prompting strategies that work well with one generation of models may be suboptimal with the next. It’s worth periodically revisiting your agent instructions to ensure they align with the capabilities of the models you currently use.
These AI constraints aren’t theoretical. They are practical limitations that become apparent as you move from simple demos to production systems. Every agent framework and approach have to address these challenges in some way, whether explicitly or implicitly.
The key insight is that these constraints interact with each other. Verbose tool outputs worsen context pollution. Complex instruction hierarchies reduce attention available for actual reasoning. Accumulated tool call history makes rule saturation more likely.
Successful production agent systems tend to be designed with these constraints in mind from the beginning, rather than trying to work around them after the fact. This often means making architectural choices that are different from what you might expect from tutorials or simple examples. In the end, our goal isn’t to convince you to use any specific agent framework, but to illustrate how understanding these constraints can inform better agent system design regardless of which tools or framework you choose.
An earlier version of this article was published on LinkedIn.
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