Agentic AI

This arXiv paper introduces Oracle Agent Memory, a system designed to handle the complexity of retaining and managing state for long-horizon AI agents. Rather than relying solely on document retrieval, the architecture treats memory as a lifecycle involving ingestion, extraction, consolidation, and revision. It leverages Oracle Database to provide durable, scoped, and retrievable state that persists across sessions and conversations.

• Moves beyond simple RAG by treating agent memory as a structured, durable lifecycle problem.
• Uses Oracle Database as the native substrate for state retention and retrieval.
• Addresses key challenges in scoping, latency, and the revision of accumulated knowledge.
• Focuses on practical deployment needs for long-horizon agent interactions.

Amazon OpenSearch Service now supports the Agent Toolkit for AWS, allowing AI coding agents like Claude Code and Cursor to manage domains and Serverless collections via natural language. This integration leverages the AWS Model Context Protocol (MCP) server to execute API calls automatically. A curated skill routes requests across migration, operations, and search capabilities.

• AI agents can now provision and manage OpenSearch domains and Serverless collections directly.
• Integration uses AWS MCP server to handle API calls based on natural language prompts.
• Supported agents include Claude Code, Kiro, and Cursor for development workflows.
• Automates migration from self-managed to fully managed OpenSearch environments.
• Enables semantic and vector search operations through natural language instructions.

This survey examines how autonomous agents evolve capabilities through experience with minimal human intervention. It introduces a system-level framework viewing agents as configurations of foundation models paired with operational scaffolds like prompts, memory, and tools. Self-improvement is formalized as a self-induced update operator that commits changes to either model parameters or these scaffold components. The work categorizes existing research by what is updated and the signals driving those updates.

• Self-improving agents are transitioning from research to production environments.
• Framework treats agents as config of foundation model plus operational scaffold.
• Improvement is formalized as self-induced updates to parameters or scaffolds.
• Prior work organized by update target and driving signals.

This paper addresses the challenge of modifying AI agent harnesses, which coordinate prompts, state, and tools. As systems evolve, developers struggle to map behavioral requirements to distributed, tightly coupled code. The work proposes methods to make these harnesses readable and editable for both human engineers and coding agents.

• AI agent harnesses are critical infrastructure often overlooked compared to the foundation model.
• Behavioral requirements are hard to map to code in large, distributed, and coupled repositories.
• Standard code search and indexing are insufficient for understanding behavioral intent.
• Improved navigability is essential for both human developers and autonomous coding agents.
• The work focuses on making harnesses editable as models and APIs continuously change.

This paper introduces Safety Sentry, a guard model that replaces binary safe/unsafe classification with a three-way EXECUTE-ASK-REFUSE routing decision. By evaluating actions at the instance level rather than the category level, it distinguishes between inherent harm and contextual appropriateness. The approach aims to reduce unnecessary interruptions while ensuring critical safety checks are enforced where needed.

• Replaces binary guardrails with EXECUTE, ASK, or REFUSE routing for finer control.
• Evaluates safety at the individual action instance level, not just the category.
• Separates inherent harm from contextual appropriateness to reduce false positives.
• Uses a lightweight guard model to minimize inference overhead for real-time agents.

MyAG introduces a graph-based architecture that decouples LLM agent construction into component, workflow, and search graphs. This separation enables flexible reuse of modules across different execution strategies and supports hierarchical composition via recursive nodes. The framework includes monitoring and visualization tools to help practitioners analyze performance and efficiency tradeoffs in agent deployments.

• Decouples agent logic, execution flow, and search strategies into distinct graph layers.
• Enables modular reuse of components across varying execution contexts.
• Supports hierarchical system design through recursive node definitions.
• Provides built-in monitoring and visualization for runtime analysis.