Goal Engineering
The future of agents is them taking on open ended goals and driving towards completion of those goals. Learn more. Context & related coverage →
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All tracked setups →Top signals · Nov 12–14, 2026 · Updated Nov 14, 22:05 UTC
From Delegation to Coordination: Learning Efficient Multi-Agent Deep Research
Multi-agent deep-research systems use delegation to parallelize search and keep long tool traces out of the main agent's context. Yet delegation helps only when task allocation and subtask execution are coordinated: t... Context & related coverage →
Skill Adapter: Personalized Modification and Online Routing of General-Purpose Skills
Large language model (LLM) agents use external skills to reuse task procedures and experience with tools. General-purpose skills describe shared execution procedures, while specific tool choices, operating practices,... Context & related coverage →
CoReMem: Cost-Internalizing Memory for Successive Coding Tasks with Prospective Valuation
Coding agents tackle successive tasks on an evolving repository, carrying forward edits and writing memory before the next task is known. Selecting memory for unknown next tasks requires balancing future usefulness ag... Context & related coverage →
Google Open Sources Ambient Quality Agent (AQuA) to Diagnose Production AI Agent Failures
Google has open-sourced AQuA, a reference implementation that analyzes production AI agent sessions, verifies recurring failure patterns, and investigates root causes against deployed source snapshots. In a 32-session... Context & related coverage →
Introducing ChatGPT Wallet
Introducing ChatGPT Wallet, a secure way for agents in ChatGPT Work, Codex, and dots to complete purchases with user approval and existing card protections. Context & related coverage →
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