agent architectures
30 articles about agent architectures in AI news
Seven Voice AI Architectures That Actually Work in Production
An engineer shares seven voice agent architectures that have survived production, detailing their components, latency improvements, and failure modes. This is a practical guide for building real-time, interruptible, and scalable voice AI.
OpenDev Paper Formalizes the Architecture for Next-Generation Terminal AI Coding Agents
A comprehensive 81-page research paper introduces OpenDev, a systematic framework for building terminal-based AI coding agents. The work details specialized model routing, dual-agent architectures, and safety controls that address reliability challenges in autonomous coding systems.
Memory Systems for AI Agents: Architectures, Frameworks, and Challenges
A technical analysis details the multi-layered memory architectures—short-term, episodic, semantic, procedural—required to transform stateless LLMs into persistent, reliable AI agents. It compares frameworks like MemGPT and LangMem that manage context limits and prevent memory drift.
8 RAG Architectures Explained for AI Engineers: From Naive to Agentic Retrieval
A technical thread explains eight distinct RAG architectures with specific use cases, from basic vector similarity to complex agentic systems. This provides a practical framework for engineers choosing the right approach for different retrieval tasks.
AI Agent Types and Communication Architectures: From Simple Systems to Multi-Agent Ecosystems
A guide to designing scalable AI agent systems, detailing agent types, multi-agent patterns, and communication architectures for real-world enterprise production. This represents the shift from reactive chatbots to autonomous, task-executing AI.
Building ReAct Agents from Scratch: A Deep Dive into Agentic Architectures, Memory, and Guardrails
A comprehensive technical guide explains how to construct and secure AI agents using the ReAct (Reasoning + Acting) framework. This matters for retail AI leaders as autonomous agents move from theory to production, enabling complex, multi-step workflows.
Beyond Simple Retrieval: The Rise of Agentic RAG Systems That Think for Themselves
Traditional RAG systems are evolving into 'agentic' architectures where AI agents actively control the retrieval process. A new 5-layer evaluation framework helps developers measure when these intelligent pipelines make better decisions than static systems.
Karpathy's AI Research Agent: 630 Lines of Code That Could Reshape Machine Learning
Andrej Karpathy has released an open-source AI agent that autonomously runs ML research loops—modifying architectures, tuning hyperparameters, and committing improvements to Git while requiring minimal human oversight.
Graph-Based AI Agents Are Revolutionizing Software Development
Researchers are developing graph-based multi-agent systems that dynamically adapt their collaboration patterns to solve complex coding problems more effectively than traditional fixed architectures.
OpenSage: The Dawn of Self-Programming AI Agents That Build Their Own Teams
OpenSage introduces the first agent development kit enabling LLMs to autonomously create AI agents with self-generated architectures, toolkits, and memory systems, potentially revolutionizing how AI systems are designed and deployed.
The Agent Revolution: How AI is Forcing a Fundamental Rewrite of Enterprise Software
Box CEO Aaron Levie predicts a seismic shift from human-operated software to AI agent-driven workflows, requiring API-first architectures and specialized file systems. This transformation will fundamentally change how SaaS companies generate revenue and structure their products.
Subagent AI Architecture: The Key to Reliable, Scalable Retail Technology Development
Subagent AI architectures break complex development tasks into specialized roles, enabling more reliable implementation of retail systems like personalization engines, inventory APIs, and clienteling tools. This approach prevents context collapse in large codebases.
Beyond RAG: How AI Memory Systems Are Creating Truly Adaptive Agents
AI development is shifting from static retrieval systems to dynamic memory architectures that enable continual learning. This evolution from RAG to agent memory represents a fundamental change in how AI systems accumulate and utilize knowledge over time.
Meta: Code Agents Improve by Reusing Short Summaries, Not Raw Logs
Meta's new paper reveals that coding agents with summary-based history reuse outperform those using raw logs, improving efficiency and success on complex tasks.
Meta Deploys Millions of Amazon Graviton CPUs for AI Agents
Meta will deploy tens of millions of AWS Graviton5 CPU cores for AI agent workloads, signaling that agentic inference favors CPUs over GPUs. The deal deepens Meta's $200B+ infrastructure push amid layoffs and cloud rivalry.
Run Claude Code in Any Sandbox with One API: AgentBox SDK
Swap coding agents and sandbox providers without changing code. Preserves full interactive capabilities (approval flows, streaming).
Stateless Memory for Enterprise AI Agents: Scaling Without State
The paper replaces stateful agent memory with immutable decision logs using event-sourcing, allowing thousands of concurrent agent instances to scale horizontally without state bottlenecks.
NVIDIA, Google Cloud Expand AI Partnership for Agentic & Physical AI
NVIDIA and Google Cloud announced an expanded partnership to advance agentic and physical AI, focusing on new infrastructure and software integrations. This builds on their existing collaboration to provide optimized AI training and inference platforms.
AI Agents Now Training Other AI Models, Sparking Autoresearch Trend
AI agents are now being used to train other AI models, creating advanced agentic systems. This development stems from Andrej Karpathy's autoresearch repository and represents early-stage automation of AI research.
A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search
A new research paper presents a reference architecture for 'agentic hybrid retrieval' that orchestrates BM25, dense embeddings, and LLM agents to handle underspecified queries against sparse metadata. It introduces offline metadata augmentation and analyzes two architectural styles for quality attributes like governance and performance.
Researchers Achieve Ultra-Long-Horizon Agentic Science with Cohesive AI Agents
A research team has developed AI agents capable of executing and maintaining coherent, long-horizon scientific research workflows. This addresses a core challenge in creating autonomous systems for complex discovery.
New Protocol Enables Self-Improving AI Agents with Auditable Lineage
Researchers have proposed a formal protocol for creating self-improving AI agent systems. The framework enables agents to autonomously evaluate and implement upgrades while maintaining auditable lineage and safe rollback options.
Your AI Agent Is Only as Good as Its Harness — Here’s What That Means
An article from Towards AI emphasizes that the reliability and safety of an AI agent depend more on its controlling 'harness'—the system of protocols, tools, and observability layers—than on the underlying model. This concept is reportedly worth $2 billion but remains poorly understood by many developers.
Chamath: AI Coding Agents Erase the '10x Engineer' Advantage
Chamath Palihapitiya argues AI coding agents are eliminating the '10x engineer' by making the most efficient code paths obvious to all, similar to how AI solved chess. This reduces technical differentiation and shifts the basis of engineering value.
OpenAI Open-Sources Agents SDK, Supports 100+ LLMs
OpenAI has open-sourced its internal Agents SDK, a lightweight framework for building multi-agent systems. It features three core primitives, works with over 100 LLMs, and has gained 18.9k GitHub stars immediately.
Autogenesis Protocol Enables Self-Evolving AI Agents Without Retraining
A new paper introduces Autogenesis, a self-evolving agent protocol. Agents can assess their own shortcomings, propose and test improvements, and update their operational framework in a continuous loop.
MCP vs CLI: The Hidden War for AI Agent Tool Integration
A fundamental architectural debate pits Anthropic's standardized Model Context Protocol (MCP) against traditional CLI execution for AI agent tool use. The choice between safety/standardization (MCP) and flexibility/speed (CLI) will shape enterprise AI deployment.
Cognee Open-Source Framework Unifies Vector, Graph, and Relational Memory for AI Agents
Developer Akshay Pachaar argues AI agent memory requires three data stores—vector, graph, and relational—to handle semantics, relationships, and provenance. His open-source project Cognee unifies them behind a simple API.
HORIZON Benchmark Diagnoses Long-Horizon Failures in GPT-5 and Claude Agents
A new benchmark called HORIZON systematically analyzes where and why LLM agents like GPT-5 and Claude fail on long-horizon tasks. The study collected over 3100 agent trajectories and provides a scalable method for failure attribution, offering practical guidance for building more reliable agents.
Omar Saro on Multi-User LLM Agents: A New Framework Frontier
AI researcher Omar Saro points out that all current LLM agent frameworks are designed for single-user instruction, creating a deployment barrier for team-based workflows. This identifies a major unsolved problem in making AI agents practically useful in organizations.