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Token optimization is the new tokenmaxxing. Here's why burning fewer tokens produces better software and why the economics of AI make this shift inevitable.

Most debugging agents fail not because the model is wrong, but because the data going in is not ready for machine consumption. Here's what data curation actually looks like in practice. When we started building Multiplayer's debugging agent, we made the same mistake almost everyone makes. We gave our coding agent access to observability data and expected it to figure out what was relevant. It didn't.

The Multiplayer debugging agent is open source under MIT. Here's why, and what it means for how you use it. Today we're open sourcing the Multiplayer debugging agent: connect your favorite coding agent to prod to fix application bugs automatically. Run it locally and eliminate PR slop. The core (session-based data capture, local-first architecture, intelligent deduplication, and coding agent integration) is publicly available under MIT, free to use, and auditable by anyone.

Dashboards, sampling, and data lakes were built for human debugging. Closing the bug-to-fix loop for AI agents requires rethinking how runtime data is collected and correlated. Observability as we know it is on its way out. For over a decade, we built telemetry stacks around a single consumer: a human, staring at a dashboard, trying to make sense of a system under stress.

The Multiplayer debugging agent is purpose-built for developers working with coding agents. It captures all the data observability tools miss and manages the whole process from bug identified to bug fixed. AI coding assistants are great at writing code. They are not great at fixing bugs in production and the reason is simple: they don’t have runtime visibility.

Custom logging can technically capture everything, but in practice, it rarely does. Coverage degrades over time, external APIs get forgotten, and during incidents, you're left asking "did anyone log this?" instead of debugging. Automatic capture solves this. If you're a technical leader, there's a good chance your team is spending significant time on custom logging… and you might not even realize how much it's costing you in productivity and incomplete debugging data.

There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data. There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data.

Better specs and clearer task decomposition are a significant step forward. But specs and plans describe intentions. What AI agents also need is visibility into what systems actually do at runtime.

The next generation of debugging doesn’t depend exclusively on the quality of AI models, but it’s heavily dependent on feeding AI tools the context they need to be useful. AI coding assistants have transformed how we write code. For example, GitHub Copilot, Cursor, and ChatGPT can generate Stripe integration boilerplate in seconds. They'll scaffold your payment flow, suggest error handling patterns, and even write unit tests.

In 2025 we focused on a simple but ambitious goal: making debugging faster, less fragmented and less manual. Check out all our releases to make that possible. 2025 was a defining year for Multiplayer. We focused on a simple but ambitious goal: making debugging faster, less fragmented and less manual. That meant meeting developers where they were already working and capturing the right context at the right time.