Scaling Agentic Workflows

Exploring the limits of context constraints. We experiment with optimizing token usage so we can scale and use long-running agentic capabilities more effectively. Honestly, our initial goal was simply to figure out how to run complex loop workflows while staying within the free tier!

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Algorithmic Context Optimization

A lot of our time is spent staring at the attention bottleneck (or what we sometimes playfully call the "Saddle"). We're experimenting with purely algorithmic, lossless ways to maximize context density, aiming to pack more signal into fewer tokens before the memory fills up.

By aggressively optimizing token usage through redundancy removal and skill distillation, we unlock the ability to scale long-running agents without hitting context ceilings. We aren't selling anything; we just enjoy building the A/B testing pipelines and releasing our optimization layers so the broader community can run smarter autonomous systems globally.

Abstract Context Compression Diagram

Network Signals

A quick look at the latest updates across the AI ecosystem.

State-of-the-Art Watch

Syndicated

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