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On 2026-06-13 @hamptonism predicts that within a year, production agents will…

2026-06-13 · 16:13 UTC ·@hamptonism ·0 min read

Brief

The post asserts that agent self-improvement—agents that analyze their production failures, create better evals from them, and iterate autonomously—will soon feel obvious. It highlights Adaline 2.0 (Arsh Shah Dilbagi) as a functioning early layer that turns traces into behaviors and auto-generates evals, tests candidate agents, and surfaces winners for human review.

Why it matters

On 2026-06-13 @hamptonism predicts that within a year, production agents will autonomously read their own failures, convert those failures into improved evaluation tasks, and iteratively sharpen without manual tracing and babysitting.

Key details

Arsh Shah Dilbagi’s Adaline 2.0 is presented as an early working example: it converts Traces → Behaviors → Issues → auto-generated Evals/Data, then generates and tests new agent candidates so engineers only review and ship the winners.

Source evidence

in a year this is gonna feel obvious.

agents that quietly read their own production failures → turn them into better evals → and keep getting sharper without you manually babysitting every trace.

early version of that is already here and it’s kinda wild to see working.

Video

Arsh Shah Dilbagi (@arshdilbagi)

Introducing Adaline 2.0 - The Agent Self-Improvement Layer

Adaline turns Traces into Behaviors,
Behaviors surface Issues,
Issues become auto-generated Evals + Data,
Adaline then generates new agent candidates and tests them.

You review the winners and ship!

Video

— https://nitter.net/arshdilbagi/status/2065826083224834345#m