# Multi Agent Systems Published articles for Multi Agent Systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agent session chains that can't escalate DevFeed: [Agent session chains that can't escalate]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [API](), [API keys](), [systems]() Tags: [agent](), [api](), [auth](), [authorization](), [backend](), [multi-agent](), [multi-agent-systems](), [permission](), [server](), [systems]() ### AI overview The article explains how Agent Auth session chaining lets an agent create separately scoped child sessions for subtasks. Each child records its parent, uses its own access and single-use refresh tokens, can be revoked independently, and cannot outlive the root session. The supplied text also states that the backend creates the chain server-side using an API key. ### Source excerpt An agent can open a child session of itself for a sub-task. The chain re-derives authority at every hop and can never outlive its root. ## Build an AI Agent (From Scratch) DevFeed: [Build an AI Agent (From Scratch)]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [AI Agent](), [Large Language Model](), [Model Context Protocol (MCP)](), [Retrieval Augmented Generation (RAG)](), [Multi Agent Systems]() Tags: [ai](), [ai-agent](), [ai-agents](), [book](), [claude-code](), [coding-assistant](), [genealogy](), [llm](), [llms](), [mcp](), [multi-agent-systems](), [rag](), [review]() ### AI overview A review of Build an AI Agent (From Scratch) by Jungjun Hur and Younghee Song describes the book's coverage of LLM agents, tool use, ReAct agents, RAG, memory, planning, code execution, multi-agent systems, and agent evaluation. The reviewer considers it a fundamental introduction, while wishing the evaluation chapter were more developed. ### Source excerpt Facts 10 chapters$29.29 (at the time of this writing)315 pages Chapters Building your first LLM agentWhat is an AI agent? ## The Essential Guide to Multi-Agent Systems DevFeed: [The Essential Guide to Multi-Agent Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-31T11:38:47Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [AI Agent](), [distributed-systems](), [systems]() Tags: [ai-agent](), [architecture](), [consistency](), [distributed-systems](), [multi-agent-systems](), [observability](), [state]() ### AI overview This guide explains what multi-agent systems are, how they differ from workflow engines and generic distributed systems, and why coordination, communication, shared state, consistency, and observability shape their architecture. ### Source excerpt Building one AI agent is hard. But what changes when you start adding more? ## A2A Is Now an Open Standard. The Data Layer Underneath It Isn't. DevFeed: [A2A Is Now an Open Standard. The Data Layer Underneath It Isn't.]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-08-19T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [agent to agent communication](), [Multi Agent Systems](), [Model Context Protocol](), [Database](), [Artificial Intelligence]() Tags: [a2a](), [agent-to-agent-communication](), [agentic](), [agents](), [ai](), [architecture](), [database](), [mcp](), [model-context-protocol](), [multi-agent-systems](), [protocol](), [standards]() ### AI overview The article argues that A2A standardizes communication between agents but leaves the underlying data and state layer undefined. It contrasts A2A's horizontal agent-to-agent communication with MCP's vertical connection between an agent and external tools or data, and argues that production systems need both alongside a database-centered state architecture. AGENTS.md is presented as another interface standard that leaves durable state outside its scope. ### Source excerpt In April 2025, Google released a protocol for agent-to-agent communication. Within three months, Google had donated it to the Linux Foundation... ## Multi Agent Systems for Platform Engineering, Patterns and Architecture DevFeed: [Multi Agent Systems for Platform Engineering, Patterns and Architecture]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-26T16:16:42Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [Platform Engineering](), [distributed-systems](), [systems](), [Model Context Protocol](), [Provisioning]() Tags: [a2a-protocol](), [agent](), [agent-orchestration](), [agent-to-agent-communication](), [ai-agents](), [architecture](), [audit-trail](), [automation](), [devops](), [distributed-systems](), [hierarchical-agent-systems](), [internal-developer-platform-automation](), [kubernetes](), [langgraph](), [langgraph-multi-agent](), [mcp](), [mcp-for-platform-engineering](), [multi-agent](), [multi-agent-architecture-patterns](), [multi-agent-systems](), [multi-agent-systems-for-platform-engineering](), [platform-engineering](), [platform-engineering-automation](), [provisioning](), [supervisor-agent-pattern]() ### AI overview A practical guide to designing multi-agent systems for platform engineering. It compares supervisor, pipeline, fan-out, hierarchical, and swarm patterns; explains how agent contracts, identity boundaries, audit trails, and verification address common failure modes; and distinguishes MCP from A2A. ### Source excerpt Studies of real agent systems put failure rates between 41 and 86 percent, and almost none of it is the model's fault. Here are the patterns that hold up for platform work, and the discipline that decides whether yours does. ## 19 Hands-On Video Guides for PMs DevFeed: [19 Hands-On Video Guides for PMs]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-07-14T14:51:34Z Content type: article Language: en Sources: [The Product Compass]() Topics: [Learning](), [Claude Code](), [codex](), [agentic-engineering](), [vs-code](), [Extension](), [Command-line interface](), [n8n](), [Multi Agent Systems]() Tags: [agentic-engineering](), [claude-code](), [cli](), [codex](), [cursor](), [engineering](), [extension](), [guides](), [learning](), [multi-agent-systems](), [n8n](), [video]() ### AI overview The article announces an Events Archive containing 19 recordings of AI product-management sessions, including demonstrations, question-and-answer segments, timestamps, free templates, and linked resources. It highlights three free sessions on Claude Cowork, Codex App, and Claude Code, and describes upcoming sessions and an AI PM Learning Program. It also reports the author's Grok Build for VS Code extension reaching more than 20,000 installs and about 1,400 daily active users. ### Source excerpt The Events Archive is live: Claude Code, Cowork, Codex, n8n, Lovable, and more. Start with the 3 free ones. ## Infrastructure Integration, AI-Agent Discovery, and Multi-Agent Systems DevFeed: [Infrastructure Integration, AI-Agent Discovery, and Multi-Agent Systems]() Original publisher: [Read original article]() Author: Russ Published: 2026-06-11T01:36:55Z Content type: opinion Language: en Sources: [rule 11 reader]() Topics: [Resilience](), [systems](), [Artificial Intelligence](), [Internet](), [AI Bots]() Tags: [ai](), [ai-agents](), [industry](), [infrastructure](), [multi-agent-systems](), [mythos](), [systems](), [worth-reading]() ### AI overview The article discusses infrastructure vertical integration as a resilience and commercial-survival issue, increased merger and acquisition activity, the challenges of building multi-agent systems, and DNS-AID, a proposed DNS-based approach to discovering AI agents. The supplied excerpt does not state the referenced findings in detail. ### Source excerpt When business volume and market capitalization cross a critical threshold, vertically integrating infrastructure ceases to be merely a cost-cutting financial tactic; it becomes an existential imperative for computational resilience and commercial survival. It looks like industry mergers and acquisition activity is in high gear lately. Building multi-agent systems right now feels painfully identical to the early, chaotic days of the micro services gold rush. The standard is called DNS-AID (Domain Name System for AI Discovery). Its premise is that the internet already solved the problem of finding things at scale forty years ago with DNS -- and that the same infrastructure should handle AI agents. Mythos is real. I know a big chunk of the industry thinks it's a marketing stunt, and I get why. I get it. But I've seen the findings, and they're bad. ## How We Built DigitalOcean Inference Router DevFeed: [How We Built DigitalOcean Inference Router]() Original publisher: [Read original article]() Author: Adil Hafeez Published: 2026-05-20T14:57:13Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Model Routing](), [Digital Ocean](), [Inference](), [Large Language Model](), [Multi Agent Systems]() Tags: [agentic-workflows](), [digitalocean](), [engineering](), [inference](), [llm](), [multi-agent-systems](), [product-updates](), [routing]() ### AI overview DigitalOcean describes its Inference Router, which uses purpose-built models and live metrics to route LLM requests to appropriate models. The article explains how routing can reduce the cost and maintenance burden of applying one frontier model uniformly across coding, analysis, debugging, and other agentic tasks. ### Source excerpt Most teams building on LLMs today make a single model decision and apply it uniformly across every request. They reach for a frontier model not because every task demands it, but because building the infrastructure to do anything smarter is hard, time-consuming, and easy to get wrong. When the tooling isn't there, the path of least resistance is to use a single model, even if it means that you end up overpaying for most tasks. Let's take an example. If you're a developer building with Cursor, Claude Code, Open Code or any coding agent today, you've already felt this. In a single session, your agent does deep codebase analysis, writes new functions, fixes bugs from test output, explains methods, searches documentation. These tasks are not equivalent but if you're on a single hardcoded model, you're paying frontier rates for all of them, including the ones that don't need it. The stakes are even higher in agentic workflows and multi-agent systems. When multiple agents are running in parallel each planning, executing, and evaluating across long-horizon tasks the cost of uniform model selection compounds with every step. Furthermore, major AI providers are moving toward token-based billing and tighter rate limits. Inference costs are about to get more expensive. The alternative is for hardcoded routing logic in the application layer with an intent classifier with the help of an LLM which adds to your cost and gets brittle fast. Even if you were to use a smaller model like Haiku to keep costs down, you're now paying for a routing call on top of every inference call. Also, accuracy takes a hit as the model is not purpose built for routing, and as your task types evolve or models change, the logic breaks in ways that are hard to catch. You've introduced double taxation: the cost of the classifier plus the cost of maintaining brittle routing code that needs updating with every change to your stack. You've turned model selection into a feature you own and maintain, which is ## Redleader revamp: How the Agentic Data Plane enables governed multi-agent AI DevFeed: [Redleader revamp: How the Agentic Data Plane enables governed multi-agent AI]() Original publisher: [Read original article]() Author: Robert Siwicki Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Streaming](), [systems](), [Cloud]() Tags: [agentic](), [ai](), [architecture](), [autonomous](), [cloud](), [multi-agent-systems](), [streaming](), [thought-leadership]() ### AI overview Redleader was revamped from a single prompt-driven agent into a governed, bounded multi-agent system running on Redpanda's Agentic Data Plane. Specialist agents collaborate through structured, durable event streams that support coordination, governance, operational tracking, and continuous improvement. ### Source excerpt How to build safe, multi-agent systems in Redpanda Cloud. ## DigitalOcean Acquires Katanemo Labs to Support Production AI Agent Infrastructure DevFeed: [DigitalOcean Acquires Katanemo Labs to Support Production AI Agent Infrastructure]() Original publisher: [Read original article]() Author: Vinay Kumar, DigitalOcean Chief Product & Technology Officer Published: 2026-04-02T12:30:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [AI Infrastructure](), [Orchestration](), [observability](), [Multi Agent Systems](), [Deployment]() Tags: [ai](), [ai-infrastructure](), [deployment](), [digitalocean](), [multi-agent-systems](), [observability](), [orchestration](), [product-updates]() ### AI overview DigitalOcean acquired Katanemo Labs and plans to integrate Katanemo Labs' Plano data plane and observability capabilities. The combined platform is intended to provide built-in orchestration, execution, and monitoring for teams building and running AI agent applications in production. ### Source excerpt At DigitalOcean, we have been vocal about our strategic shift: we are building the world's premier Agentic Inference Cloud. Our mission is to provide the foundation where AI-native enterprises build and run production inference at scale. Today, I am thrilled to announce a significant step in that journey: we have acquired Katanemo Labs, Inc., a leader in agentic AI infrastructure. By integrating Katanemo Labs, we are extending our platform further into the critical operational layer of agentic systems. Their technology pairs perfectly with DigitalOcean's philosophy of operational simplicity, predictable economics, and scalable performance. We are also proud to welcome Salman Paracha, co-founder and CEO of Katanemo Labs, as our new Senior Vice President of AI. His team's deep expertise in model research and open-source infrastructure development will be instrumental as we accelerate our mission to provide developers with an agentic inference cloud for running AI in production. Bridging the Production Gap In 2025, the center of gravity in AI shifted from experimentation to production. As the industry moves toward real-world deployment, the hardest problems have shifted from model access and accuracy to the challenges of running reliable, safe, and observable systems at scale. 61% of developers view closing the gap between prototype and production as their single greatest challenge, as found in our Currents research report. McKinsey research shows that fewer than 10% of AI use cases ever make it past the pilot stage. That same research identifies observability as a key capability for enabling large-scale, intelligent agent ecosystems to operate safely.** By bringing Katanemo Labs into DigitalOcean, we are directly addressing this "production gap," delivering core AI primitives--building blocks like orchestration, workflow execution, and safety protocols that make multi-agent systems reliable, observable, and fast to ship into reliable business assets. Accelerating the A ## От мозга к мультиагентным системам: как устроены Foundation Agents нового поколения DevFeed: [От мозга к мультиагентным системам: как устроены Foundation Agents нового поколения]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2025-07-24T21:44:07Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [Large Language Model]() Tags: [agents](), [ai](), [alignment](), [deep-learning](), [jailbreak](), [large-language-models](), [llm](), [machine-learning](), [multi-agent-systems](), [prompt-engineering](), [prompt-injection](), [rag](), [reasoning](), [reinforcement-learning](), [safety](), [tag-b0a411324cb6](), [tag-b6914c0b0244]() ### AI overview This Russian-language analytical article explains the concept of Foundation Agents based on the research paper "Advances and Challenges in Foundation Agents." It discusses brain-inspired agent architecture, cognition, learning, reasoning, memory, world models, rewards, perception, action, self-improvement, multi-agent collaboration, and agent safety issues including jailbreaks, prompt injection, hallucinations, misalignment, poisoning attacks, and privacy. ### Source excerpt Аналитический центр red_mad_robot разобрал объёмную научную статью "Advances and Challenges in Foundation Agents" от группы исследователей AI из передовых международных университетов и технологических компаний. Работа предлагает новый взгляд на текущее состояние и развитие "интеллектуальных агентов", которые могут адаптироваться к множеству задач и контекстов. Рассказываем, какие идеи лежат в основе Foundation Agents, с какими проблемами предстоит столкнуться, и что ждёт нас в будущем. Читать далее ## A2A Is For UI DevFeed: [A2A Is For UI]() Original publisher: [Read original article]() Published: 2025-06-14T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Protocol (disambiguation)](), [Multi Agent Systems](), [ui](), [AI Agent](), [HTTP](), [client](), [JSON](), [Remote Procedure Call (RPC)]() Tags: [agent](), [ai](), [ai-agents](), [http](), [json](), [json-rpc](), [multi-agent](), [multi-agent-systems](), [protocol](), [ui]() ### AI overview This commentary argues that Google's A2A protocol can be understood as a user interface for interactions between humans and agents, and as a bridge to more complex multi-agent systems. It describes A2A as an HTTP endpoint with JSON-RPC methods, message parts, task state, and optional webhooks. ### Source excerpt There's a lot of skepticism around A2A, Google's Agent-to-Agent protocol. A lot of that is well earned. I mean, they launched a protocol with zero implementations. But a lot's changed, and it's worth taking a look again.