Instead of relying on lossy LLM-based summarization, architect agent memory into three tiers: an ephemeral scratchpad for immediate tasks, a deterministic state machine for history (e.g., Redis), and a semantic anchor (e.g., vector store) for global knowledge lookup.

The concept isn't about fitting a massive codebase into one context window. Instead, it's a sophisticated architecture using a deep relational knowledge graph to inject only the most relevant, line-level context for a specific task at the exact moment it's needed.

The leaked architecture shows a sophisticated memory system with pointers to information, topic-specific data shards, and a self-healing search mechanism. This multi-layered approach prevents the common agent failure mode where performance degrades as more context is added over time.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

To prevent performance degradation from overly large prompts ("context rot"), recursive language models offload context to an external environment. For a coding agent, this is the file system; for Marimo Pair, it's the live Python runtime. The agent can then access this information on-demand, keeping its primary context clean and focused.

Instead of just expanding context windows, the next architectural shift is toward models that learn to manage their own context. Inspired by Recursive Language Models (RLMs), these agents will actively retrieve, transform, and store information in a persistent state, enabling more effective long-horizon reasoning.

Seemingly complex features like long-term memory and skill creation are fundamentally clever systems for managing an AI's limited context window. The "harness" efficiently loads and unloads relevant information (memories, skills) at the precise moment it's needed, rather than keeping it all in context constantly.

"Context Engineering" is the critical practice of managing information fed to an LLM, especially in multi-step agents. This includes techniques like context compaction, using sub-agents, and managing memory. Harrison Chase considers this discipline more crucial than prompt engineering for building sophisticated agents.

Tasklet completely re-architected its agent, moving from feeding chat history into the LLM to treating the file system as the primary context. The agent now receives hints and pointers to relevant files, enabling it to handle infinitely long histories and larger contexts beyond the token window.