Building Blocks Are Different
The number one reason AI agents fail in the real world is simple: we try to build them like we build traditional software, and the parts are completely different. A normal piece of code is like a predictable gear in a machine. An AI agent is more like a clever but sometimes forgetful new team member. It interprets, it guesses, and it can get confused.
Building a powerful agent isn't about finding the perfect prompt. It's about being a better architect and creating a system where a thinking, reasoning component can succeed. This requires a new set of patterns.
Build a Team, Not a Single Hero
Our first instinct is often to build one giant "do-everything" agent and give it a hundred different tools. This is a recipe for failure. With every tool you add, you increase the chances that the agent will pick the wrong one, get confused, or fail in a complex way that’s impossible to debug.
A more robust approach is to build a system of multiple, smaller, specialized agents.
- Start with a Whiteboard: Before writing any code, list out all the jobs you need the system to do. Group these jobs into logical roles, like "Researcher," "Writer," or "Code Executor."
- Build One Agent at a Time: Solve your most pressing problem with one focused agent that does its one job extremely well.
- Create a Workflow: Once you have multiple agents, you add a "routing" agent that acts like a project manager, passing the task to the right specialist at the right time.
This way, each part of your system is simple, focused, and much easier to fix when something goes wrong.
Actively Manage the Agent's Memory
An agent's "context" is its working memory. The common mistake is to just keep adding information to it, thinking more data is always better. This leads to a cluttered and confused agent. Actively managing this memory is one of the most important jobs for a builder.
- Prevent Information Overload: Don't just dump entire documents into the context. Use techniques to find the most relevant snippets first. Periodically, have the agent summarize its work so far to clean out old, unnecessary details.
- Stop Bad Information from Spreading: A single wrong fact can "poison" the agent's memory, causing it to make a chain of bad decisions. By keeping the context clean and focused, you limit the damage of any single mistake.
- Let It Learn from Errors: This is a crucial pattern. When an agent tries to run code and fails, don't just hide the error. Feed the error message back into the agent's context. Often, the agent can read the error, understand what went wrong, and fix its own code on the next try. This creates a resilient, self-correcting system.
Test for Success, Not Just Accuracy
Testing an AI is tricky because it doesn't give a simple "pass" or "fail." It can be technically correct but completely wrong for your business, or it can sound confident while making things up. A new approach to testing is needed.
- List Your Failure Modes: Create a checklist of all the specific ways the agent can fail. For a legal agent, this might include "missed a critical clause" or "misinterpreted a date." This gives you concrete things to test for.
- Use Real Experts to Grade It: Software engineers are not qualified to judge an agent's medical or financial advice. The only way to know if your agent is truly working is to have subject matter experts (SMEs) review its outputs. These experts create a "golden set" of correct answers that you can test against.
- Build a Test Suite from Real Data: The best test cases come from your actual users. Log the agent's real-world interactions (especially the failures), and use this data to build a test suite that reflects how the agent will actually be used.
Build a Safe Environment for Action
An agent that can take actions like executing code, sending emails, or deleting files is powerful but also dangerous. Safety cannot be an afterthought; it must be part of the initial design.
- Use a Sandbox: Never let an agent run code directly on your production systems or your own computer. A sandbox is a secure, isolated environment where the agent can execute code and use tools without any risk of causing permanent damage.
- Set Up Guardrails: You need filters that watch what goes into the agent and what comes out. Input guardrails can block malicious prompts or attempts to trick the agent. Output guardrails can stop the agent from leaking sensitive data or generating harmful content.
Conclusion
Building reliable agents shifts the focus of our work. The job is less about writing code line-by-line and more about being a systems architect. Your primary tasks are designing a clean workflow, managing the flow of information, defining what "correct" means, and building a safe environment. By adopting these patterns, we can move from building brittle bots that fail unpredictably to engineering reliable systems that we can trust.