delegation

This paper proposes a compositional authorization framework for agentic AI systems, introducing primitives for delegation, scope attenuation, and recursive permission chains to govern autonomous AI agents.

This paper introduces Capability Self-Assessment (CSA) for LLMs, formulating it as a policy-learning problem. Experiments show that reinforcement learning effectively teaches models to recognize their own limits and delegate queries they cannot solve, outperforming supervised fine-tuning and generalizing well out-of-distribution.

This paper studies how humans decide when to delegate to AI and when to adopt AI suggestions in cooperative question answering, finding that confirmation bias drives suboptimal trust decisions such as under-reliance on correct AI outputs.

A comprehensive guide teaching non-coders how to build AI agents using Claude and Cowork without writing any code, explaining the core components and providing step-by-step instructions.

DecisionBench introduces a standardized benchmark for evaluating emergent delegation in long-horizon multi-agent workflows, providing a substrate with task suites, peer models, and multi-axis metrics to isolate orchestration capabilities.

The author reflects on how AI tools became truly useful when they stopped requiring step-by-step instructions and instead autonomously handled multi-step tasks, shifting from being micromanaged to being delegated to.

A reflection on the tradeoff in AI agent design: the point at which agents become useful by having real-world capabilities is the same point at which they become risky, requiring careful boundary setting for delegated authority.

A new method for orchestrating agents is being worked on, featuring delegation plans and subagents that can run locally or in Dockerized cloud environments, with message passing between them.

Delegare is a product that enables AI agents to be given spending power while maintaining control for the user. It appears to be a financial delegation tool for AI agent workflows.

pi-subagents is a tool that allows Pi to delegate tasks to focused child agents for code review, implementation, and other workflows, enabling parallel and background jobs.