Overview
The same model can perform very differently in two different environments. In a difficult codebase, the agent may spend large amounts of context and time locating relevant code, resolving unclear interfaces, retrying failed changes, and inferring undocumented expectations. In a well-designed codebase, the path from task to implementation is shorter.
Harness quality can be improved through:
- Clear prompts that state the goal and decision boundaries.
- Skills that provide a reusable procedure at the right moment.
- Tools that let the agent inspect, modify, test, and review the system.
- Sandboxed execution that contains the agent's actions.
- Documentation that points to the correct modules and conventions.
- Tests and guardrails that provide feedback.
- A codebase architecture that is easy to navigate and change.
This has a direct relationship to token use. If the codebase is easy to understand and modify, the agent needs fewer attempts and less exploratory reasoning. A less capable or cheaper model may then complete work that would otherwise require a more capable model to overcome environmental friction.
A harness can also be kept relatively agent-agnostic. Instead of building a workflow around the quirks of a single model release, the system can rely on software practices that remain useful across models: clear interfaces, small tasks, tests, documentation, isolation, and review.
Agent experience, or AX, is the experience an agent has while working in the codebase. It parallels developer experience, or DX. A codebase that is understandable and changeable for human developers often provides many of the same advantages to agents. Better skills, a stronger harness, and a better-structured codebase all improve AX.