The inverse of grill-me: instead of Claude extracting what you know, it teaches
you what it knows. It quizzes before it explains, schedules reviews with a simplified FSRS and can drill you on any topic, a document, or your own codebase
This paper evaluates three control strategies for untrusted agents: deferral to trusted models, resampling, and critical action deferral. Initial testing showed resampling and critical action deferral achieving 96% safety. However, adversarial testing revealed resampling crashes to 17% safety when attackers can detect resampling or simulate monitors, while critical action deferral remained robust against all attack strategies.
HaluMem introduces the first benchmark for evaluating hallucinations in agent memory systems at the operation level. Through three evaluation tasks (memory extraction, updating, and question answering), it reveals that existing memory systems generate and accumulate hallucinations during early stages, which then propagate errors downstream. The benchmark uses two datasets spanning different context scales to systematically reveal these failure modes.
OpenHands SDK provides a complete architectural redesign for building production software development agents. It balances simplicity (few lines of code for basic agents) with extensibility (custom tools, memory management) while delivering seamless local-to-remote execution, integrated security, and connections to various interfaces (VS Code, command line, APIs).
TL;DR
Problem: "Tool overload" is a critical bottleneck for AI agents. Providing an LLM with a large, static list of tools bloats the context window, degrading performance, increasing costs, and reducing accuracy.
Solution: Implement a "select, then execute" architectural pattern. Use a lightweight "router" agent to first retrieve a small, relevant subset of tools for a specific task. Then, a more capable "specialist" agent uses that curated set to execute the request.
Benefits: Lower latency and cost (fewer tokens), higher tool-selection precision, a scalable architecture for large tool catalogs, and improved reliability.
Pattern: This pattern is a form of Retrieval-Augmented Generation (RAG) applied to tools, often called Retrieval-Augmented Tool Selection (RATS). It can be combined with State-Based Gating for even greater precision.
How: This post provides a complete, production-aware implementation using Google's Agent Development Kit (ADK).