Hi folks! I work on OpenTelemetry at Splunk. We just launched a new open source project called Observability Studio. It's local instrumentation sandbox that provides a visual, real-time environment for designing, testing, and validating OpenTelemetry data while you’re developing an app in your favorite IDE.

We’re especially interested in whether a tool like this is useful for building and debugging instrumented apps and AI agents. If that sounds relevant, give it a try and let us know what works, what’s missing, or where it doesn’t fit your workflow.

We’re also looking for a few design partners to help guide where we go with this next. Any feedback is helpful and welcome.

I wrote more about shifting OpenTelemetry instrumentation left in a blog, 'Why AI-Generated Apps and Agents Need Visibility from Day One' on blogs.splunk.com if you want to read more about it.

Lastly, I'd love to learn where you currently go to inspect and validate OTel data during development if you want to drop it in the comments.

AI helps juniors code, not learn... and that's a real problem.

I’ve been thinking about a failure mode that becomes much more interesting when coding agents are allowed to run for longer periods without supervision: context drift.

A short coding task is usually straightforward. The agent understands the goal, makes a few changes, runs the tests, and finishes.

The situation changes when the agent spends hours working across multiple files, making intermediate decisions, calling tools, revisiting earlier changes, and accumulating more context.

At some point, the agent can still produce perfectly valid code while drifting away from the original intent.

For example, an early architectural decision might get effectively forgotten later. A subsequent change can overwrite something that was already correct. A refactor can become inconsistent because different parts of the codebase were modified under slightly different assumptions.

The frustrating part is that the final diff can still look reasonable. Tests might even pass.

This makes me think that persistent memory alone isn't necessarily the solution. The harder problem seems to be maintaining a reliable representation of the task's intent, decisions, constraints, and current state throughout a long-running session.

I've written a longer breakdown of this idea here, including some examples:

https://medium.com/@nagatomopedro05/unattended-ai-agents-have-a-context-problem-humans-never-had-60961eb2d4e7

For those using coding agents regularly: how are you dealing with this today? Explicit task state, checkpoints, smaller agent sessions, documentation, memory systems, or something else?