title: "Construire un harness efficace pour des travaux d'entreprise avancés"

Building reliable AI agents for complex, multi-step enterprise tasks requires overcoming challenges related to context window limitations and token usage. A proposed solution is a code agent harness that prompts a large language model (LLM) to generate executable code, like Python, for orchestrating workflows. This code-first approach allows for more sophisticated logic, such as loops and conditionals, compared to standard tool-calling methods. To improve efficiency, the system uses techniques like tool truncation and progressive disclosure, which shorten tool descriptions and only reveal necessary details as needed. These optimization strategies significantly reduce token consumption and improve the agent's performance on complex tasks.