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agentic scaffolding

Creating a structured process for an AI agent to follow, with broken tasks and managed context, makes it more reliable. This framework coordinates different components like planners and verifiers, helping the agent handle complex jobs more deliberately than using its raw abilities alone.

The papers consistently find that this scaffolding drives performance more than the model itself. Improvements in task decomposition, context handling, and verification loops lead to better outcomes, especially for multi-step or evidence-gathering tasks. Such structures let agents succeed where they otherwise would fail, and often with greater efficiency.

Papers
33
Released code
1
First seen
Sept 2026
Latest
Nov 2026

32 papers in the last two months, against 1 in the two before.

Start here

Three papers to read first, in the order they came out
  1. 1Robot policies falter as manipulation tasks stretch across more subgoals

    Robot manipulation policies succeed on single subgoals at 85.4% but collapse on multi-subgoal tasks; tool-selection orchestration improves success but physical execution errors remain the bottleneck.

    Oct 2026
  2. 2Orchestrating coding agents broadens autonomous computational research beyond optimization

    The Agentic Scientist orchestrates coding agents with rubric verification to automate computational research and discovery.

    Oct 2026
  3. 3AI teacher adapts its hints as a language model learns, boosting performance across four domains

    AutoScaffold uses a frozen teacher LLM to automatically design adaptive training scaffolds that improve language model RL performance across domains.

    Nov 2026

How fast it is moving

JunJulAugSeptOctNov so far

Often studied with

agentic evaluationMeasuring AI agents' performance in complex tasks, highlighting failures in planning, monitoring, and real6 togetherverificationChecking outputs for correctness before accepting them, often to catch errors or reward hacking.6 togetheragent harnessA system that structures, verifies, monitors, or controls how an AI agent interacts with external tools, environments, or execution runs.5 together

Who is working on it

Amazon 3KAIST 2Tsinghua University 2The Chinese University of Hong Kong, Shenzhen 2

The papers

Most central to this idea first, not most recent.

Concepts are extracted from each paper and reused across the corpus, so this page grows on its own as the desk reads.