@ankrgyl: 2025: engineer LLM APIs into your harness 2026: engineer harnesses to work in your agent
Summary
A brief prediction that in 2025 engineers will integrate LLM APIs into their test harnesses, and in 2026 they will design harnesses to work within their agents.
View Cached Full Text
Cached at: 06/23/26, 07:45 AM
2025: engineer LLM APIs into your harness
2026: engineer harnesses to work in your agent
Similar Articles
best of the best agentic harnesses do this…
The author shares insights on building effective agent harnesses: the best ones minimize LLM reliance for trivial tasks and reserve LLMs for complex reasoning, distinguishing genuine harnesses from simple wrappers.
HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?
HarnessDev evaluates LLMs by their ability to build and evolve execution harnesses, revealing significant variations in performance and poor transferability across models.
A lot of conversation around Harness Engineering, What does that even mean?
A discussion about the buzz around Harness Engineering and AI agent Harness, questioning whether the industry is moving away from LLMs deciding agent responses.
Self-Harness: Harnesses That Improve Themselves
Self-Harness introduces a new paradigm where LLM-based agents iteratively improve their own operating harness by mining model-specific weaknesses, proposing harness modifications, and validating them through regression testing, achieving substantial performance gains on Terminal-Bench-2.0 across multiple base models.
@omarsar0: // Self-Harness: Harnesses That Improve Themselves // (bookmark this one) Most of the agent scaffolds we rely on today …
This paper introduces Self-Harness, a new paradigm where LLM-based agents iteratively improve their own operating harness—prompts, tools, and control flow—without human engineers or stronger external agents, achieving significant performance gains across multiple models.