What humans do is keep moving from imperative to declarative in programming languages, and in how agents are prompted via goals and loop definitions.
Self-awareness and loop-closing are core capabilities of an AI agent.
Action Space
Designing the agent's action space to match the model's capabilities is key. This includes developing an AskUserQuestion tool, transitioning from TodoWrite to the Task tool, and building context through progressive disclosure.
The key point is not to simply give an agent powerful tools, but to provide tools that are carefully designed to match the model’s inherent capabilities. As concrete evidence, this presents the three-stage evolution of the
AskUserQuestion tool. Early attempts tried adding parameters to ExitPlanTool or parsing specific Markdown formats, but ultimately converged on a dedicated tool-calling interface that the model can understand most easily and respond to most clearly. It also emphasizes that tools should evolve as model performance improves, illustrated by the transition from a simple to-do list (TodoWrite) to a Task Tool that supports inter-agent collaboration and dependency management. It points out the limitations of RAG approaches that inject all context up front, and explains the importance of Progressive Disclosure, where the agent uses tools such as Grep to discover and build its own context—maximizing performance by filtering out irrelevant information and focusing on what matters.Thariq on Twitter / X
https://t.co/nKTDfC7zMm— Thariq (@trq212) February 27, 2026
https://x.com/trq212/status/2027463795355095314
Dan Farrelly | Inngest.com on Twitter / X
https://t.co/mcAz5Kjmjj— Dan Farrelly | Inngest.com (@djfarrelly) March 2, 2026
https://x.com/djfarrelly/status/2028556984396452250
Meta-Harness Self-Improving AI
Meta-Harness is an outer-loop system that automatically searches over harness code. Its core objective is : for a fixed LLM and a task distribution , it seeks the harness that maximizes the expected reward of trajectories . When multiple objectives (e.g., accuracy and context cost) are involved, evaluation is done via Pareto dominance.
In each iteration, what changes is not the prompt but the entire agent system code → including tool policy, loop logic, error handling, and context strategy.
Meta-Harness
Meta-Harness automatically optimizes model harnesses — the code determining what to store, retrieve, and present to an LLM — surpassing hand-designed systems on text classification, math reasoning, and agentic coding.
https://yoonholee.com/meta-harness/

Seonglae Cho