Debugging complex AI Agent was a nightmare? Then read this.

At NextRows, our AI agents are complex systems. They interact with language models, run code in secure sandboxes, and browse the web to accomplish tasks. With so many moving parts, a simple question can become incredibly difficult to answer: "What just happened?"

When an AI agent doesn't behave as expected, digging through logs is often a nightmare. Was the issue with the LLM prompt? Did a tool fail in its sandbox? Was there a network error? Answering these questions quickly is essential for building a reliable product. This is where Laminar, our observability platform, comes in.

The Challenge: Understanding the Black Box

Our system, at its core, has an orchestrating agent built on Next.js. This agent is the "brain" that drives the whole operation. As you can see in our architecture, it communicates with several components:

LLM Providers: To get instructions and process information.

E2B Sandboxes: To execute code and shell commands safely.

Browserbase: To perform web-based actions.

A single user request can trigger a complex chain of events across all these services. Without the right tools, tracing the lifecycle of that request is nearly impossible. It's like trying to understand a conversation by only hearing every fifth word.

What is Observability?

Observability is more than just logging. It's about being able to ask arbitrary questions about your system without having to ship new code. A good observability platform gives you a complete, holistic view of your system's behavior. For us, that platform is Laminar.

Laminar is designed specifically for modern AI applications. It understands that an AI agent's workflow isn't a simple, linear path. It's a series of nested calls, tool executions, and model interactions.

How We Use Laminar

Our central orchestrating agent is the single source of truth for our observability data. At every critical step of a task, the agent emits detailed information to Laminar. This includes:

Traces: A trace represents the entire journey of a single request, from the moment you click a button to the final result appearing on your screen.

Spans: Each trace is made up of spans, which are the individual operations within that journey. A span could be an LLM call, a bash command running in an E2B sandbox, or a database write.

Tool I/O: For every tool our agent uses, we log the exact inputs and outputs. We can see precisely what data was sent to a tool and what it returned.

This continuous stream of data from our agent gives Laminar a complete picture of everything happening inside our system in real-time.

The Benefits: From Mystery to Mastery

Integrating Laminar isn't just about collecting data; it's about what we can do with it.

1. Instant Debugging

When a user reports an issue, we no longer have to guess. We can pull up the exact trace for their request and see the entire sequence of events. We can pinpoint the exact span that failed and inspect the tool's input/output to understand why. This reduces our debugging time from hours to minutes.

2. Performance Optimization

Laminar allows us to see which parts of our system are slow. Is a particular LLM call taking too long? Is a specific tool creating a bottleneck? By visualizing the duration of each span, we can identify and fix performance issues, leading to a faster experience for you.

3. A More Reliable Product

By understanding why and how things fail, we can build a more resilient system. We can add better error handling, improve our LLM prompts, and optimize our tools. The result is a more reliable and predictable product that you can depend on.

Conclusion

For any team building complex, multi-component AI systems, observability is not a luxury—it's a necessity. Laminar gives us the deep visibility we need to move fast, fix problems, and build a world-class product. It turns the "black box" of AI into a transparent, understandable system.

Join Us

If you are passionate about building the future of AI and creating incredible user experiences, we want to hear from you. Please reach out to support@nextrows.com with your story and resume. We'd love to have you on our team.