Essays by Ry Walker

The hardest unsolved problem in agent infrastructure is not compute or sandboxing. It is context — and most of that context lives in people, not repos.

Models keep getting better, but agents without deep codebase and organizational context are just expensive autocomplete. Context engineering is the bottleneck nobody has productized.

Every quarter a new model writes marginally better benchmark code. And every quarter enterprise teams stall on the same context problems. The hard part is the engineering around the AI.

A skill described in a markdown file is not a skill. It is a wish. Real skills need executable tools, automated tests, schema validation, and deep system integration.

Enterprise teams do not want magic agents. They want control over which submodules load, which tools run, and what the agent remembers — because they have been burned by black boxes before.

Shared agents with a single configuration are fundamentally broken. Each user gets a personal copy that learns through interactions — and an organizational layer that locks in what consistently works.

General-purpose agent memory is still a research problem. The opportunity is workflow-scoped learning that compounds — pick the constraint, and the memory problem stops being intractable.

The interesting part of an agent stopped being the prompt years ago. Orchestration, persistence, tool integrations, and policy enforcement are where the complexity — and the value — lives now.

The maintenance burden of running agents is not one thing. It splits into two categories with opposite cost curves, and only one of them ever ends.

Submodules are a specific pain point, but they illustrate a universal truth. Enterprise codebases are not simple, and agents that cannot handle them cannot handle enterprise software.

The coding-agent debate is dominated by model capability. Wrong bottleneck. Context splits into structural, navigational, and operational layers — and most agents are missing all three.

A scene from every engineering org operationalizing agents. The task was trivial. The PR was wrong in a way no human on the team would ever get wrong. It is not a model problem.