# multi-agent Published articles for multi-agent. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram ๐Ÿ›ฐ by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## 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. ## Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks DevFeed: [Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-09-12T17:56:36Z Content type: opinion Language: en Sources: [Byte-Sized Design]() Topics: [Microservice](), [Architecture & Design]() Tags: [agent](), [architecture](), [microservices](), [multi-agent]() ### AI overview An opinion article comparing multi-agent architecture with microservices. It discusses what still works, three places the approach breaks, and one current heuristic. ### Source excerpt The parts that still work, the three places it breaks, and one heuristic for right now ## When to Split a Single AI Agent into Multiple Agents DevFeed: [When to Split a Single AI Agent into Multiple Agents]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-09-12T05:19:14Z Content type: opinion Language: en Sources: [The T-Shaped Dev]() Topics: [Artificial Intelligence](), [Architecture & Design](), [Microservices]() Tags: [agent](), [ai](), [ai-agents](), [microservices](), [multi-agent]() ### AI overview The article argues that multi-agent systems can be worthwhile when a single agent suffers from expanding tool choices, conflicting system instructions, and excessive blast radius. It recommends starting with one agent and using measurable signs to determine when to split into specialized agents managed by a supervisor. ### Source excerpt When one agent should become many, when it shouldn't, and the supervisor pattern that makes the split work. (8 min) ## Grok Bot vs. OpenClaw: How I replaced my entire agent stack DevFeed: [Grok Bot vs. OpenClaw: How I replaced my entire agent stack]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-02T12:03:39Z Content type: comparison Language: en Sources: [Lenny's Newsletter]() Topics: [OpenClaw](), [multi-agent](), [Bot](), [Pull Request](), [soc 2 compliance](), [Monitoring](), [Support](), [Slack]() Tags: [agents](), [bots](), [helpdesk](), [monitoring](), [multi-agent](), [openclaw](), [rbac](), [slack](), [soc-2-compliance]() ### AI overview A podcast episode compares Grok Bot with OpenClaw and explains why the host migrated a multi-agent setup to Grok Bot. It covers bots for inboxes, Slack, PR management, SOC 2 monitoring, customer support, family tasks, and personal activities, along with the migration process. ### Source excerpt Watch now | ๐ŸŽ™ I've been running Grok Bot for several weeks, and I've killed every OpenClaw I had. Here are the nine bots doing real work across my inbox, my kids' school pickups, my PR queue, and my wardrobe ## Build a Multi-Agent GTM Intelligence System DevFeed: [Build a Multi-Agent GTM Intelligence System]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-25T20:26:56Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Code](), [API]() Tags: [agent](), [api](), [building](), [code](), [multi-agent](), [pipelines](), [research]() ### AI overview A hands-on tutorial for building a multi-agent go-to-market intelligence pipeline with Seltz. It explains how combining structured people and company-news records can help identify timely re-engagement opportunities. ### Source excerpt ...explained with code. ## 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... ## 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. ## A Multi-Agent AI System for Reviewing Code Before Pull Requests DevFeed: [A Multi-Agent AI System for Reviewing Code Before Pull Requests]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-07-26T07:01:38Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [ai code review](), [Code review](), [Artificial Intelligence](), [Pull Request]() Tags: [ai-code-review](), [code-review](), [multi-agent](), [pull-request]() ### AI overview The author describes a multi-agent AI code review system that finds, verifies, and ranks issues before a pull request is opened. ### Source excerpt My multi-agent AI code review system finds, verifies, and ranks issues before I open the pull request. ## Agentic Autonomy Levels DevFeed: [Agentic Autonomy Levels]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-03T14:30:33Z Content type: article Language: en Sources: [Elevate]() Topics: [Artificial Intelligence](), [Orchestration](), [Agent Skill](), [Claude](), [Claude Code](), [codex]() Tags: [agent](), [agentic](), [agentic-engineering](), [agents](), [ai](), [claude](), [claude-code](), [codex](), [delegation](), [multi-agent](), [orchestration](), [parallel](), [scopes](), [trust](), [verification]() ### AI overview This article proposes a two-axis model for evaluating agentic engineering: agency, describing how independently a single agent operates, and orchestration, describing the skill of coordinating multiple agents. It argues that a single autonomy ladder is insufficient for multi-agent work and emphasizes matching autonomy levels with appropriate verification. ### Source excerpt A working model of autonomy for agentic engineering ## Agentic Autonomy Levels DevFeed: [Agentic Autonomy Levels]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-02T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Artificial Intelligence](), [Orchestration](), [Agent Skill]() Tags: [agentic](), [agentic-engineering](), [ai](), [delegation](), [multi-agent](), [orchestration](), [scale](), [scopes](), [trust](), [verification]() ### AI overview The article argues that agent autonomy should be evaluated on two separate axes: agency, describing how independently an agent pursues a goal, and orchestration, describing the ability to coordinate multiple agents. It emphasizes choosing an autonomy level per task and defending that choice through verification. ### Source excerpt The action in agentic engineering has moved from prompting to operating. Autonomy isn't one ladder, it's two axes (agency and orchestration) and six levels you move between per task. The real question is what level a task deserves, and what verification makes that level defensible. ## Learning faster with Antigravity DevFeed: [Learning faster with Antigravity]() Original publisher: [Read original article]() Author: Andrew Brogdon Published: 2026-07-01T04:08:53Z Content type: article Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [Agent Skill](), [ai-coding](), [Python](), [Development](), [SDK]() Tags: [agent-skill](), [agentic-ai](), [ai-coding](), [architecture](), [development](), [flutter](), [gemini](), [google-antigravity](), [ios](), [multi-agent](), [python](), [sdk](), [web-development]() ### AI overview The article describes using Antigravity and a structured, iterative workflow to learn Python-based Agent Development Kit (ADK) concepts and build Flutter frontends for an existing multi-agent research coordinator. The author created a reusable developer skill, flutter_frontend_for_adk, with reference documents covering agent analysis, frontend usage, architecture, and design. ### Source excerpt Dash enjoying AntigravityCreating Flutter frontends for ADK How can I build a Flutter frontend for an agent when that agent is built with an SDK and a language I've never used before? This was the challenge I faced when approaching a Python-based agent written with Agent Development Kit (ADK). With limited experience in Python and no prior exposure to the ADK framework, building a client that integrates with the backend server presented a significant learning curve. Plus, even if I could get a coding agent to crank out something that worked, finishing the project without understanding the code was also a form of failure. After a few false starts, though, I found an answer. Using a structured, iterative workflow with my AI coding partner, Antigravity, I created a reusable developer skill that codified what I learned with each go-round. I started from scratch, generated notes about the code, created multiple apps that connected to the deep_search agent (a multi-agent research coordinator from the official ADK samples repository), and incrementally built up the skill and my own understanding. At times, I was using multiple agents at the same time, an "author" agent to create the skill with me, and a "coder" agent to use that guidance to build frontends. What I ended up with was an agent skill called flutter_frontend_for_adk. It includes five reference docs that guide Antigravity through a sequence of phases, each one ending in a deliverable. The first phases generated the following notes files, so that I could structure how the "coder" agent thought about the task as it analyzed the agent and prepared to generate the app: AGENT_INTERFACE_NOTES.md -- Notes taken during an analysis of the agent's source code. What is it meant to accomplish and how is it constructed? What are the interfaces and APIs this agent exposes and how do they work? FRONTEND_USAGE_NOTES.md -- The first spec. What should the frontend do and how should users interact with it? FRONTEND_ARCHITECTURE_NOTE ## How I Passed Claude Architect Certification DevFeed: [How I Passed Claude Architect Certification]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-06-25T16:03:08Z Content type: tutorial Language: en Sources: [Big Tech Careers]() Topics: [Claude](), [Claude Code](), [Model Context Protocol](), [API](), [SDKs](), [multi-agent](), [Tech Careers]() Tags: [api](), [claude](), [claude-code](), [learn](), [mcp](), [multi-agent](), [sdk](), [tech-careers]() ### AI overview A personal account of preparing for and passing the Claude Certified Architect exam. The author compares an original six-week study roadmap with what they actually followed, describing how they compressed the plan into two weeks after their workload eased and used courses, projects, exam scenarios, preparation exercises, and a practice exam. ### Source excerpt Resources and study tips ## 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. ## Antigravity 2.0 vs Android Studio - making TagSpotter DevFeed: [Antigravity 2.0 vs Android Studio - making TagSpotter]() Original publisher: [Read original article]() Published: 2026-05-31T08:21:45Z Content type: opinion Language: en Sources: [maiatoday]() Topics: [Android Studio](), [ide](), [Command-line interface](), [Gradle](), [Emulator](), [Git](), [cpu]() Tags: [agent](), [ai](), [ai-agents](), [android](), [android-studio](), [antigravity](), [cli](), [code](), [command-line](), [cpu](), [debug](), [emulator](), [git](), [gradle](), [multi-agent]() ### AI overview An Android development article compares how Antigravity 2.0 and Android Studio fit into agent-assisted workflows. It describes Antigravity as suited to broad exploration, planning, parallel agent work, worktrees, and command-line builds or emulator runs, while Android Studio remains preferable for editing, debugging, previewing, and other IDE conveniences. The author also notes performance costs from multiple Android Studio instances and competing tools. ### Source excerpt How does Antigravity and Android Studio work together? What do I do where? There are as many workflows and preferences as there are Android engineers starting with light mode vs dark mode. This is not an attempt to provoke a holy war. What I have noticed is that in the days BA (Before Agents) I used to open Android Studio and do most things in there, edit, debug, preview, git and do CLI things. Now its not like that anymore. Click here if you want to skip the rant ๐Ÿ˜ค and just jump to Antigravity experiment Today in Android Studio I can run 1 agent or 2 if one is on a command line, max 3 if the third is just the Ask/chat tab. Or I can have a ton of terminals open with multiple agents juggling work trees. Or I can use one of the many multi agent solutions like Conductor, Superset or Antigravity 2.0 that are popping up like mushrooms. Then I could open multiple Android Studio versions in the worktrees I spun up with the agents. If I am in full agent mode and I just quickly want to edit something, I can do it in a small editor in the orchestrator tool but very soon I miss the creature comforts of Android Studio. But opening multiple instances of Android Studio is super slow (looking at you gradle sync and indexing). If my orchestrator environment is Antigravity 2.0 I can either jump back into Antigravity IDE or open the project in AndroidStudio. The problem Arrgh so many chat boxes everywhere with agents waiting to eat my tokens! So many tool combos all fighting for RAM and CPU cycles on my machine! I just want to add a setting to a file, I might as well edit it in vi. ok deep breath. I don't have a solution and I'm sure the internet has many suggestions so let me just figure out whre Antigravity 2.0 shines and what is better to do in Android Studio. Use Antigravity The mindset - broad, explore, plan, hands off, chatty, parallel work In Antigravity it is broader picture, start an app. Exploration and planning. Multiple agents doing things in worktrees. Agents are editing ## Advanced Patterns: Grok + Hermes for Complex Workflows DevFeed: [Advanced Patterns: Grok + Hermes for Complex Workflows]() Original publisher: [Read original article]() Author: hi@meedamian.com (Damian Mee) Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Damian Mee | Blog | Portfolio | About]() Topics: [Automation](), [sessions](), [Tool](), [Raspberry Pi]() Tags: [advanced](), [authoring](), [context](), [delegation](), [long-running](), [memory](), [multi-agent](), [pi](), [productivity](), [skills](), [workflows]() ### AI overview This developer post presents advanced workflow patterns for using Grok inside Hermes Agent. It discusses planning and sub-agent delegation, generating reusable SKILL.md files, and combining context handling with Hermes' memory tool for long-running projects. The author says these patterns improved productivity on Raspberry Pi projects and personal automation. ### Source excerpt Disclaimer: This post was generated by Grok 4.3 running inside Hermes Agent. Advanced Grok + Hermes Workflows Once you have the basics down, here are some powerful patterns for using Grok inside Hermes Agent. Multi-Agent Delegation Grok excels at planning. Use it to orchestrate sub-agents: hermes delegate --goal "Refactor the auth module" --model grok-4.3 Skill Authoring with Grok Grok is fantastic at writing reusable skills. Ask it to generate SKILL. ## Inside Claude Code, OpenAI Codex, and HuggingFace's ML Engineer Agent : ๐Ÿ“š Tokenizer #26 DevFeed: [Inside Claude Code, OpenAI Codex, and HuggingFace's ML Engineer Agent : ๐Ÿ“š Tokenizer #26]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-04-30T13:12:59Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Claude Code](), [Artificial Intelligence](), [codex](), [Machine learning](), [OpenAI](), [Open Source Models & Datasets](), [Library](), [data]() Tags: [agent](), [ai](), [claude-code](), [codex](), [coding](), [ml](), [multi-agent](), [newsletter](), [openai](), [resources]() ### AI overview This issue of The Tokenizer curates AI and machine-learning resources, including papers on multi-agent organization and iterative reasoning, videos about OpenAI Codex and agent complexity, a source-code walkthrough of Claude Code, tools for coding-agent context and computer control, and Hugging Face's open-source ML engineer agent. ### Source excerpt This week's most valuable AI Resources ## How to design agentic tools for work DevFeed: [How to design agentic tools for work]() Original publisher: [Read original article]() Author: Jenny Xie Published: 2026-04-29T16:44:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Google](), [Orchestration](), [User experience (UX)](), [dashboards]() Tags: [agentic](), [agentic-experience](), [connectors](), [design](), [gemini](), [google](), [google-cloud](), [how-to](), [interface](), [multi-agent](), [workflows]() ### AI overview The Gemini Enterprise team explains how it designed an agentic experience for complex, collaborative business work. The approach emphasizes simplicity, transparency, user intervention, and tools such as an AI Inbox, visual dashboards, connectors, and shared project spaces. ### Source excerpt The Gemini Enterprise team shares their approach to making complex, multi-agent workflows feel simple, intuitive, and trustworthy. ## 130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check DevFeed: [130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check]() Original publisher: [Read original article]() Author: Eugene Petrenko Published: 2026-04-28T00:00:00Z Content type: article Language: en Sources: [Eugene Petrenko]() Topics: [AI Agent](), [Claude](), [codex](), [Artificial Intelligence](), [issue tracker](), [Orchestration](), [Command-line interface](), [Graphs](), [Requirements]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-coding](), [cli](), [code](), [eugene-petrenko](), [graph](), [issue-tracker](), [jonnyzzz](), [mcp](), [mcp-steroid](), [multi-agent](), [orchestration](), [pipeline](), [requirements](), [run-agent](), [tag-41199d53f463]() ### AI overview The article describes using a swarm of Claude, Codex, and Gemini agent CLIs to research 130+ release-roadmap issues and produce implementation specifications. The process used 818 agent runs over four days at an approximately $576 cost, followed by a mid-release comparison of the research with the team's actual work. ### Source excerpt We turned one release's roadmap -- 130+ umbrella-epic issues -- into fully-researched implementation specs using a swarm of three different agent CLIs (Claude, Codex, Gemini): 818 runs, ~155M+ tokens, ~$576, four days. Then, mid-release-cycle, we ran a snapshot to compare the research against reality -- with no expectation that anything was "done" yet. Here is the pipeline, the numbers, and the honest verdict. ## Using Claude Code and Codex to iteratively improve coding plans DevFeed: [Using Claude Code and Codex to iteratively improve coding plans]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2026-04-25T07:00:00Z Content type: tutorial Language: en Sources: [Kaushik Gopal's Site]() Topics: [Claude](), [Claude Code](), [codex](), [coding]() Tags: [agent](), [agents](), [claude](), [claude-code](), [codex](), [evaluation](), [multi-agent]() ### AI overview The article describes Agent Kombat, a workflow in which Claude Code and Codex independently draft plans for the same coding requirement, critique each other's plans, and revise them over several rounds. A separate judge agent may then synthesize the plans or run a focused replay. ### Source excerpt Most multi-agent coding setups I see today look like task parallelism. You split the work, hand each piece to a different agent, and merge the results at the end. That is useful. I do it too. But I've been trying a different approach and really liking it: Put two agents on the same problem and make them argue constructively before I trust the plan. The manual version is simple: I spin up Claude Code with Opus 4.7 and ask it to draft a plan: my-plan-claude.md. Then I spin up Codex with GPT-5.5 and ask it to draft a plan for the same requirement: my-plan-codex.md. Now the useful part starts... I ask Claude to read the Codex plan, steal whatever is better, update its own plan, and give me a concrete list of deficiencies in the Codex plan. Then I take those deficiencies back to Codex and ask it to do the same thing: read Claude's updated plan, steal the good parts, defend or fix the weak parts, and update my-plan-codex.md. Then back to Claude. I do this about three times. This works annoyingly well. The final plan is usually much better than the first one-shot plan from either model. Each model forces the other one to look at the problem from a slightly different angle. I started calling this Agent Kombat. And because copy-pasting between two terminals gets old fast, I built a small program that runs the loop for me. Agent Kombat Download the script here The loop ## The loop has only a few rules: Both agents start from the same requirement. Each agent writes its own plan before seeing the other plan. Each round, the agent must name what is stronger in the other plan. Each round, the agent must update its own plan. Each round, the agent must list concrete deficiencies in the other plan. After a few rounds, a separate judge (agent) decides whether to synthesize or run one focused replay. The "concrete deficiencies" part does most of the work. If I just ask, "what do you think?", the models get polite. They compliment each other, merge a few phrases, and call it convergence. ## Multi-Agent System Reliability DevFeed: [Multi-Agent System Reliability]() Original publisher: [Read original article]() Author: Alex Ewerlรถf 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 ## Trusting AI agents: A reinsurance case study DevFeed: [Trusting AI agents: A reinsurance case study]() Original publisher: [Read original article]() Author: Sophia Barnes Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [data-processing](), [datasets](), [decision-making](), [parallel](), [file]() Tags: [agents](), [ai](), [ai-agents](), [automate](), [case-study](), [data-processing](), [excel](), [how-to](), [insurance](), [multi-agent](), [pricing](), [risk](), [workflows]() ### AI overview This case study explains how a multi-agent AI system with human-in-the-loop safeguards automates reinsurance data workflows. The system parses unstandardized Excel submission packs, matches catastrophe events with historical records, creates cedant loss records, and flags changes to existing data. ### Source excerpt Learn how to build a reliable multi-agent AI system with human-in-the-loop safeguards using Temporal. A detailed case study on automating complex reinsurance data workflows. ## OWASP Top 10 for Agentic Applications 2026: Key Takeaways & How to Take Action DevFeed: [OWASP Top 10 for Agentic Applications 2026: Key Takeaways & How to Take Action]() Original publisher: [Read original article]() Author: jpitts@goteleport.com (Jack Pitts) Published: 2025-12-15T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Security](), [Securing AI](), [ai security](), [Application Security](), [Artificial Intelligence]() Tags: [2026](), [agentic-ai](), [agentic-security](), [agents](), [ai](), [apis](), [application-security](), [cloud](), [databases](), [multi-agent](), [security]() ### AI overview This article explains the OWASP Top 10 for Agentic Applications 2026, summarizing AI security risks in autonomous, tool-using, and multi-agent systems and outlining mitigation steps. It introduces Agent Goal Hijack, in which malicious content can manipulate an agent's objectives or task selection. ### Source excerpt Learn how OWASP Top 10 for Agentic Applications 2026 defines the biggest AI security risks, with clear explanations and actionable steps to protect your systems. [Next page]()