Towards a Harness That Can Do Anything
eardatasci.github.io
July 15, 2026
7 min read
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58/100
Summary
A good harness for LLMs should be intuitive for the agent, transparent for self-development and auditing, and facilitate seamless interaction beyond the chat pane. Recent considerations focus on enhancing LLM capabilities and user experience.
Key Takeaways
- A good harness for LLMs should be intuitive, transparent, lean, flexible, and capable of error and update survival without memory degradation.
- The core prompt for LLMs should be minimal, allowing the model to dynamically choose which skills to load at runtime to reduce cognitive load.
- Harness-level failures can be addressed and fixed at runtime, while LLM-level failures require mitigation strategies through the harness.
- The Unix/Linux environment is proposed as a suitable model for developing an agentic harness due to its historical design principles that emphasize simplicity and modularity.
Community Sentiment
Positives
- Deterministic workflows are the key to success with long-running tasks, ensuring agents stay within their failure modes and perform reliably.
- The emphasis on behavioral-driven development (BDD) is spot on; it helps define clear expectations that guide AI tools to achieve correct behavior.
- Using a structured framework for agentic coding allows for better management of complex processes, which is crucial as we integrate AI more deeply into development.
Concerns
- The term 'harness' feels overly buzzwordy and lacks clear definition; it raises skepticism about whether we're just riding a hype wave.
- There's a concern that the idea of a 'generic' harness might be misguided, as domain-specific solutions are already proving to be more effective.
- Comments about the vague promise of doing 'anything' with AI evoke skepticism, suggesting that we should focus on mastering specific tasks instead.
