By providing a model with a few core tools (context management, web search, code execution), Artificial Analysis found it performed better on complex tasks than the integrated agentic systems within major web chatbots. This suggests leaner, focused toolsets can be more effective.

Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.

Getting high-quality results from AI doesn't come from a single complex command. The key is "harness engineering"—designing structured interaction patterns between specialized agents, such as creating a workflow where an engineer agent hands off work to a separate QA agent for verification.

The LLM itself only creates the opportunity for agentic behavior. The actual business value is unlocked when an agent is given runtime access to high-value data and tools, allowing it to perform actions and complete tasks. Without this runtime context, agents are merely sophisticated Q&A bots querying old data.

The success of tools like Anthropic's Claude Code demonstrates that well-designed harnesses are what transform a powerful AI model from a simple chatbot into a genuinely useful digital assistant. The scaffolding provides the necessary context and structure for the model to perform complex tasks effectively.

An AI coding agent's performance is driven more by its "harness"—the system for prompting, tool access, and context management—than the underlying foundation model. This orchestration layer is where products create their unique value and where the most critical engineering work lies.

Early agent development used simple frameworks ("scaffolds") to structure model interactions. As LLMs grew more capable, the industry moved to "harnesses"—more opinionated, "batteries-included" systems that provide default tools (like planning and file systems) and handle complex tasks like context compaction automatically.

Judging an AI's capability by its base model alone is misleading. Its effectiveness is significantly amplified by surrounding tooling and frameworks, like developer environments. A good tool harness can make a decent model outperform a superior model that lacks such support.

When testing models on the GDPVal benchmark, Artificial Analysis's simple agent harness allowed models like Claude to outperform their official web chatbot counterparts. This implies that bespoke chatbot environments are often constrained for cost or safety, limiting a model's full agentic capabilities which developers can unlock with custom tooling.