The Best Model Isn’t Enough: Harnesses, Context, and Better Prompts
We used to believe that a better model automatically meant better results. And that’s true to some degree, but the reality is that most people aren’t using all the available tools and techniques to get the most out of the model. For example, context management is key. Providing good AGENTS.md and CLAUDE.md files gives agents a solid baseline from the start. The harness is also important because…
The notion that a superior AI model inevitably leads to superior results has been a prevalent belief, although this is only partially accurate. The truth of the matter is that many individuals fail to leverage all the available tools and techniques to maximize the potential of the model. A critical aspect lies in context management.
By providing well-structured AGENTS.md and CLAUDE.md files, agents can establish a robust foundation from the outset. The harness is another crucial element because the model constitutes just a part of the system. The harness governs the context the model receives, the tools it can utilize, its permissions, memory, feedback loops, and evaluation methods.
Tools such as Strands Agents enable the construction of a comprehensive harness, incorporating tools, system prompts, plugins, specialized agents, context management, tracing, and evaluations. For instance, Claude Code serves as an exemplary coding harness, integrating the model with repository instructions, file and terminal tools, permissions, MCP integrations, and automated workflows.
While a superior model can contribute positively, a robust harness is instrumental in equipping the model with the understanding of its environment, enabling appropriate actions, verifying its work, and handling unforeseen challenges. Prompt engineering does not stand as the primary methodology for achieving superior outcomes. In fact, I would argue that the harness holds paramount importance currently—but this does not negate the significance of crafting effective prompts.
Several prompt-engineering recommendations can help enhance results: be clear and direct, be specific, utilize XML tags when appropriate, incorporate few-shot examples, and establish an evaluation pipeline for your prompts. By employing these techniques, models can deliver more accurate and efficient outcomes.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
