# Multi Agent Systems Multi-agent systems are collections of autonomous AI agents that coordinate and collaborate through shared communication, tools, and protocols to perform complex tasks and achieve collective goals. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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? ## 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... ## Durable, flexible multi-agent systems DevFeed: [Durable, flexible multi-agent systems]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [Orchestration](), [systems](), [Langgraph](), [Persistence](), [Google]() Tags: [agents](), [distributed-system](), [google-adk](), [langgraph](), [multi-agent](), [orchestration](), [persistence](), [temporal-voices]() ### AI overview This article describes a multi-agent delivery fleet built with Google ADK and LangGraph, using Temporal for orchestration, persistence, retries, and resumability. It explains how to keep human oversight durable when agents or workers disconnect or when workflows wait for human responses. ### Source excerpt Running a multi-agent delivery fleet on Google ADK and LangGraph, and what it takes to make the human in the loop durable. ## The Orchestrator's Tax DevFeed: [The Orchestrator's Tax]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-28T13:10:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Multi Agent Systems](), [systems](), [Claude Code](), [incident]() Tags: [agents](), [delegation](), [incident](), [memory](), [multi-agent](), [parallelism](), [workflow]() ### AI overview Rahul Garg argues that the main cost of subagents in long-running multi-agent work is not necessarily execution time or parallelism, but the orchestrator's limited working memory. Drawing on an exploratory Claude Code session involving four subagents, the article examines delegation, context overhead, and the need for explicit delegation rules. ### Source excerpt Subagents get justified by time saved and parallel execution, but Rahul Garg explains that's not what matters most. Every token in the orchestrator's context is competing for its attention, and the real value of a subagent is what it keeps out of that context. Subagents should be treated as a tool for protecting the orchestrator's working memory, offloading reasoning it doesn't need to hold onto. Doing this well means giving the orchestrator explicit ground rules for when and how to delegate. more... ## How to build deep research agents using Temporal and Braintrust DevFeed: [How to build deep research agents using Temporal and Braintrust]() Original publisher: [Read original article]() Author: Martin Bergman Published: 2026-06-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [observability](), [Pydantic](), [parallel](), [retry](), [Large Language Model]() Tags: [agents](), [build](), [how-to](), [multi-agent](), [observability](), [parallel](), [research](), [retry](), [tracing]() ### AI overview This tutorial explains a multi-agent deep research pipeline built with Temporal and Braintrust. It covers planning, query generation, parallel web search, report synthesis, and the use of Durable Execution, evals, and observability to handle timeouts, partial failures, and difficult debugging. ### Source excerpt Deep research agents are fragile in production. Here's how Temporal and Braintrust make them resilient with Durable Execution, evals, and tracing. ## 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 ## 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 ## Multi-Agent System Reliability DevFeed: [Multi-Agent System Reliability]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-02-19T20:41:37Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [Multi Agent Systems](), [LLMs](), [Artificial Intelligence](), [systems]() Tags: [architecture](), [llms](), [multi-agent](), [parallel](), [reliability-engineering](), [systems-engineering]() ### AI overview This article presents four architecture patterns--hierarchy, consensus, adversarial debate, and knock-out--to improve the reliability of multi-agent systems. It explains that underlying LLMs remain prone to hallucination, logical fallacies, and context drift, and that multi-agent architectures can amplify these errors and make debugging harder. ### Source excerpt 4 patterns to tame multi-agent systems for reliability ## Building Multi-Agent Systems with Temporal: Routing and Task Delegation DevFeed: [Building Multi-Agent Systems with Temporal: Routing and Task Delegation]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2025-08-27T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [Orchestration](), [context](), [context window](), [Routing (disambiguation)]() Tags: [agent](), [ai](), [architecture](), [context-window](), [how-to](), [multi-agent](), [orchestration]() ### AI overview This article explains how Temporal can support multi-agent systems through durable orchestration. It covers agent routing, which switches users between specialized agents, and task delegation, which assigns responsibilities to specialist agents. ### Source excerpt Multi-agent architecture helps create powerful patterns. Learn how Temporal can be used to make multi-agentic systems even better. ## 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.