Built an agent workstation where the environment does the structural reasoning so the LLM doesn't have to

Been building Atlarix — a desktop environment specifically designed for coding agents — and wanted to share the core architectural insight with this community since it's directly relevant to agent design. The problem we kept hitting: agents lose coherence on large codebases because they're doing too much structural reasoning from raw text. "Where does auth happen?" shouldn't require reading 50 files — it should be a graph query. So instead of injecting raw code, Atlarix parses the repo into a node/edge graph (rooms = files, beacons = symbols, edges = imports/calls) via oxc-parser for TS/JS and WASM tree-sitter for Python/Go/Rust. The agent calls get\_blueprint to navigate architecture, then reads specific files only when it needs them. The practical result: smaller models (7B local) perform significantly better on architecture-aware tasks because the environment carries the structural load. The model just reasons and navigates — it doesn't reconstruct architecture from scratch on every turn. Other design decisions we landed on: \- Mode-aware tool allowlists (Explore mode literally can't write files — not in the registry) \- Approval queue on every destructive action (file writes, terminal commands) \- Stepped context compaction so long agent sessions don't lose the thread \- On-demand MCP rather than loading all integrations upfront Free tier supports local models (Ollama, LM Studio). Curious how others in this community are handling the structured context problem — are you building environment layers or relying on the model to reason from raw text?