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What if improving an AI agent’s performance had less to do with choosing a better model and more to do with the system built around it?
In one experiment, LangChain improved its coding agent’s benchmark score from 52.8% to 66.5% without changing the underlying model.
The difference came from harness engineering: the tools, context management, execution logic, and feedback mechanisms that turn an LLM into a reliable agent.
In this episode, the focus is on why harness engineering is gaining attention, how it works, and why it changes the way agent performance should be evaluated.
You’ll learn:
* Why are better prompts no longer enough? Understanding the challenges of long-running agents, from context degradation and lost progress to unreliable tool use and verification.
* What actually makes an agent harness? Exploring the components behind autonomous agents, the approaches used by OpenAI and Anthropic, and the emerging engineering patterns.
* Can the same model deliver radically different results? Examining real benchmark experiments, the impact of harness design, and why leaderboard scores may reveal less about model capabilities than they appear to.
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