Glassbox: Making AI Agent Context Observable
How Glassbox finds provably dead context in coding-agent sessions and removes it with a validated, lossless fork instead of a lossy summary.
How Glassbox finds provably dead context in coding-agent sessions and removes it with a validated, lossless fork instead of a lossy summary.
Why Pi's minimal, extensible terminal harness beats heavier agent workflows by making the model, tools, session, and product surface composable.
A technical look at Rote's tool-boundary recorder, typed playbook DAG, assertion-gated executor, environment fingerprints, and planned self-healing repair loop.
The gap I kept hitting: every existing way to plug Crustdata into an agent is pull-only —> search, enrich, fetch. Nothing turns the real-time data into a *trigger*..
How attention dilution, vague quantifiers, and structural noise silently degrade your LLM outputs — and what the research says about fixing it.
Graphify gives SWAP semantic priority, graph-grounded arbitration, and pre-claim coordination across the repo.
Letta treats the context window like L1 cache, not memory, and builds an actual memory hierarchy around that idea.