scaffolding

A practitioner discussion exploring whether long-running AI agent failures stem from model capabilities or from the scaffolding around them, highlighting error compounding, context pollution, and weak self-correction as key failure modes.

Boris Cherny, creator of Claude Code at Anthropic, advises favoring general AI models over specific ones, noting that scaffolding improvements are often temporary and superseded by new models.

Anthropic engineers revealed a simple trick using external memory scaffolding where Claude agents update a file with their mistakes and improvements to enhance performance over time without retraining.

OpenAI's GPT-6 Astra offers enhanced capabilities, allowing the removal of scaffolding for efficiency, but safety metrics and enterprise controls highlight the critical need for control scaffolding to prevent unintended outcomes.

NavMCP is an agentic scaffolding framework that integrates vision-language models with navigation foundation models to enable persistent long-horizon physical-world exploration, achieving state-of-the-art results on benchmarks and real-robot tasks.

The article criticizes current AI agents as overhyped and fundamentally limited, arguing that they are essentially LLMs with scaffolding and that their reliance on next-token prediction makes them unreliable for long-horizon planning and accountability.

This paper studies strong-to-weak capability transfer at test time, showing that stronger models can build inference-time harnesses that nearly double weaker models' performance without parameter updates.

An open-source CLI tool that scaffolds production-ready AI agent projects in seconds, simplifying setup for Python developers.

The paper introduces DCAS, a backend-substitution interception layer that decouples CLI agent scaffolding from backend models, enabling cross-scaffold evaluation. It shows that planning-aware fine-tuning on a small dataset improves model generalization across non-training scaffolds.

This design-based study investigates how pedagogical scaffolding can help ethnic minority preparatory students shift from passive consumption to critical co-creation with Generative AI in prompt engineering tasks, resulting in improved prompt self-efficacy and active gatekeeping of AI-generated content.

A project-structure scaffold for organizing Generative AI applications, separating configuration, data, examples, notebooks, tests, and core source modules. It is open-source under Apache 2.0.

This survey provides a systematic framework for self-improving autonomous agents, categorizing approaches into foundation model improvement and scaffolding improvement, and reviews applications and evaluation methods.

A developer argues that the harness (critics, scaffolding) around an AI model is more important than the model itself, sharing an example where a 27B model with good critics became usable for coding work.

SelfCompact is a scaffolding approach that lets language models autonomously decide when and how to compact long agent traces, achieving better performance with reduced token costs compared to fixed-interval methods.

An analysis arguing that companies fail at AI because they focus on the model rather than the foundational layers—process design, governance, knowledge architecture, human judgment, and feedback loops—which are the true sources of value. The article cites Nadella's 'token capital' concept, Apple's model-swappable Siri, and survey data showing a wide gap between strategy and execution.

This paper studies human-AI team coordination in shared workspaces using the Collaborative Gym and DiscoveryBench tasks, finding that adding collaborators can lower performance without proper structure. Scaffolding with shared group memory and human-in-the-loop gates improves performance, especially in three-person teams.

This paper introduces SIA, a self-improving AI loop that combines scaffold rewriting and weight updates (via LoRA) to enhance task performance. Tested on three diverse tasks, it outperforms setups using only scaffold improvements.

An independent researcher introduces Epistemic Lattice Tethering (ELT), an inference-time scaffolding framework that extends coherent LLM threads to 325k–1M tokens by applying epistemic and ontological governance.

A Hugging Face blog post that defines and clarifies key terms in the AI agent field, such as scaffolding, harness, context engineering, and tool use, aiming to standardize vocabulary amidst rapid evolution.

Anthropic's applied AI team shared in a workshop how to build agents that can run for hours, with the core being context management, planning and self-validation, as well as the co-evolution of models and supporting tools.