Introduction

What does “debugging an agent” even mean?

Unlike deterministic code, errors in an agent (or any LLM based system) aren’t traceable. You have to analyze the generated responses to see what went wrong and go through the trace to see where things went wrong. These errors can largely be attributed to issues in:

• prompts
• tools/APIs passed to an agent

Identifying and rectifying these issues with prompts and tools is what agent debugging is all about.

How does Neatlogs help debug an agent?

NeatLogs helps teams debug AI agents faster by closing the gap between developers and domain experts. Instead of vague feedback in spreadsheets, teams collaborate directly on agent traces—which turns evaluations into actionable fixes. By centralizing context, NeatLogs helps teams take agents to production twice as fast, getting them to revenue sooner.

How is Neatlogs different from an agent observability tool?

Neatlogs is not built for observability, it’s built for collaboration between teams building an agentic automation. There are 2 things Observability tools get wrong:

1. They assume debugging is just a developer problem. It’s not. Without domain experts (a person who is an expert of the process that an agent will automate), debugging is just blind guesswork.
2. They alienate domain experts by creating a UX that’s far too overwhelming for a non-technical person.

Existing tools track agent activity but don’t make it easier to collaborate—forcing teams to use Slack and spreadsheets, spreading context and conversations across multiple platforms.

With NeatLogs, everything is centralized, there's no back-and-forth, and teams find it easier to pinpoint actionable insights—cutting iteration time in half, and making AI agents more reliable in production.

Who is it for?

Any team building an agent can use Neatlogs to iterate faster and collaborate with internal or external stakeholders.

How much effort would go into integrating Neatlogs with my project?

Not much. You can get full observability in just a few lines of code.

import os
import neatlogs

neatlogs.init(
    api_key=os.environ["NEATLOGS_API_KEY"],
    endpoint=os.environ["NEATLOGS_ENDPOINT"],
    workflow_name="my-agent",
    instrumentations=["openai"],
)