What interested me most wasn’t the model itself, but the surrounding system design: automated context retrieval, evaluation loops, and memory that improves the agent over time.
I’ve been experimenting with recreating a similar setup, but with a different goal: making the configuration more accessible for any company.
The result is an open-source YAML + Markdown framework where you define context sources, tools, and behavior explicitly instead of writing Python code. The idea is to make agent context easier to reason about, version, and iterate on, especially for data teams.
One thing I keep seeing in practice is that “memory” problems are often less about storage and more about structure + retrieval strategy.
Vector search helps sometimes, but for a lot of agent workflows we’ve had better results with explicit context organization (files, metadata, rules) rather than semantic similarity alone.
Curious how you’re thinking about memory updates over time — append-only vs rewriting summaries?
This is super helpful — most writeups skip over the actual communication steps, so seeing the All-to-All flow laid out makes it much clearer.
Curious from your experiments: at 1M+ context, does communication start dominating vs compute?
I keep seeing cases where bigger context windows are technically possible but don’t translate into better results unless the context is very structured, so I wonder where the real scaling limit ends up being in practice.