Building an agent has become remarkably easy. You can get a first version working in days. But getting it into production, and keeping it reliable once it’s there, can still take months.
I learned why while building a CX agent for Wingify few years ago.
When it failed, engineering would show that it had followed its instructions precisely. And yet CX would say the outcome was wrong. Working out what went wrong involved navigating multiple Slack threads, screenshots, spreadsheets, emails, and recurring meetings. Sprint after sprint came and went, and our agent stayed in staging.
Every month, our CEO would say: “Your agent should be live by now.”
Every month, we agreed. We still didn’t know what to do next.
More visibility hasn’tsolved reliability
The industry has tried to throw more traces, tests, evals, alerts, dashboards, and automated judges at the problem. Engineers can now see almost everything an agent did.
But seeing isn’t fixing.
- Traces are the source of truth, but the evidence is buried in noisy, repetitive spans while customer feedback and business context live somewhere else.
- If you hand the entire raw trace to a coding agent, the context explodes. And you pay more for worse reasoning.
- Evals don’t close the loop either. A single LLM judge is too shallow for complex work and too expensive to run over every production run.
- Meanwhile, the people who know which outcome is right for the business are outside the workflow entirely.
Engineering still has to find failures, recover the context, investigate the root cause, decide what to change, test the fix, and verify that it held.
Reliability is still amanual process
It should be a continuous loop: from failure, to evidence, to business context, to fix, and back to production.
That’s why we built neatlogs.
It connects what happened in production with what should have happened, then does the work of turning failures into fixes (and keeping them fixed).
In neatlogs, people do just two things: give feedback and approve fixes. neatlogs does the rest.
Anyone can build an agent now. Making one reliable should be just as quick and easy. We’re building neatlogs to make that possible.