# context window A context window is the amount or range of tokenized text an AI or large language model can consider when generating a response. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How LLMs Handle Memory Through Context and Surrounding Applications DevFeed: [How LLMs Handle Memory Through Context and Surrounding Applications]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-15T15:31:12Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [context window](), [App](), [long-context]() Tags: [article](), [context](), [context-window](), [cost](), [latency](), [llms](), [memory]() ### AI overview LLMs do not usually retain personal or persistent memory between interactions. Surrounding applications create the appearance of memory by storing messages, maintaining summaries, retrieving relevant information, and supplying it to the model. As conversations grow, this processing increases cost and latency, while context-window limits require older information to be removed, summarized, or stored elsewhere. ### Source excerpt In this article, we will learn how LLMs handle memory so that they are useful to end users in performing complex tasks that require conversation and holding context. ## Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI DevFeed: [Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-14T23:05:28Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [context](), [repo](), [context window](), [Developer experience](), [GitHub](), [Linear]() Tags: [ai](), [context](), [context-window](), [developer-experience](), [github](), [linear](), [product](), [repo](), [team](), [tools]() ### AI overview The article examines Together AI's AI-oriented product team and describes four parts of its working stack: a shared context repository, reusable skills for research and product documentation, an orchestrator for product leaders, and agent evaluations. It explains how shared context files and workflows support collaboration across product areas, including automated weekly status updates using Linear, GitHub, and strategy documents. ### Source excerpt I got a truly AI-pilled product team to demo the 4 key tools in their stack ## AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026) DevFeed: [AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-09-14T15:56:33Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Frontier Model](), [OpenAI](), [context window](), [browser](), [ChatGPT](), [codex](), [macOS](), [Windows](), [Mobile]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-elastic-block-store-amazon-ebs](), [amazon-opensearch-service](), [amazon-quick-suite](), [aws](), [aws-lambda](), [aws-outposts](), [aws-transform](), [bedrock](), [browser](), [chatgpt](), [codex](), [context-window](), [frontier-model](), [google-play](), [gpt](), [kiro](), [launch](), [macos](), [mobile](), [openai](), [week-in-review](), [windows]() ### AI overview This AWS Weekly Roundup highlights the general availability of OpenAI GPT-6 Astra on Amazon Bedrock, describing its reasoning, writing, design, computer-use, browser-use, and million-token context-window capabilities. It also covers the Amazon Quick desktop app for macOS and Windows, including synchronized conversations and agents across desktop and mobile, plus other AWS launches and updates. ### Source excerpt There's a particular energy to mid-September in New York. Pumpkin spice lattes are flowing, temperatures are dropping, and it's nearly sweater weather. The city is back at full speed, and so is the AWS launch calendar. This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon [...] ## SwiftUI Agent Skill: Install and use with AI coding tools DevFeed: [SwiftUI Agent Skill: Install and use with AI coding tools]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-09-14T11:49:33Z Content type: article Language: en Sources: [SwiftLee]() Topics: [SwiftUI](), [Agent Skill](), [ai-coding](), [Code quality](), [context window](), [Command-line interface]() Tags: [agent-skill](), [ai-coding](), [ai-development](), [cli](), [code-quality](), [context-window](), [installation](), [open-source](), [swiftui]() ### AI overview This article introduces an open-source SwiftUI Agent Skill for AI coding tools. The skill helps agents build or refactor SwiftUI views, improve generated code quality, and load focused references only when they are relevant to a task. It also explains installation, updates, and compatibility considerations for the skills command-line tool. ### Source excerpt A SwiftUI Agent Skill that helps you build better views or refactor existing ones. It's the reality we're in today, and I honestly can't live without it anymore myself. Several skills helped me improve the code quality produced by agents, and I'm happy to introduce you to my open-source skill for SwiftUI. Before reading this ... -> The post SwiftUI Agent Skill: Install and use with AI coding tools appeared first on SwiftLee. ## Claude Managed Agents: How They Work and Where They Fit DevFeed: [Claude Managed Agents: How They Work and Where They Fit]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-09-14T11:29:41Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [Claude](), [Artificial Intelligence](), [sdlc](), [context window](), [Network](), [Processes]() Tags: [agent](), [agents](), [ai](), [claude](), [context-window](), [infrastructure](), [network](), [platform](), [sandbox](), [sdlc](), [software](), [tools]() ### AI overview This article explains Claude Managed Agents, a hosted runtime and managed agent harness operated by Anthropic. It describes how the harness coordinates tools and execution, the sandbox provides isolated command and file access, and the session preserves durable task history outside the model's context window. It also discusses the additional platform layer needed to connect multiple agents across a business process and the SDLC. ### Source excerpt What Claude Managed Agents are, how the runtime works, and how platform teams connect several agents across the SDLC. ## Designing Reliable AI Agent Memory for Stale Facts and Policy Changes DevFeed: [Designing Reliable AI Agent Memory for Stale Facts and Policy Changes]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-09-12T12:10:58Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [AI Agent](), [context window](), [context]() Tags: [ai-agent](), [context](), [context-window](), [memory]() ### AI overview This article explains that AI agent memory should be treated as evidence rather than truth, especially when policies or other facts change. It recommends attaching version, scope, source, and authoritative-data checks to memory, while keeping the context window limited to the information needed for a task. ### Source excerpt Learn how to design reliable AI agent memory that handles stale facts, policy changes, scoped retrieval, conflict resolution, and safe deletion. ## Enable on-demand expertise with Agent Skills in Genkit Go DevFeed: [Enable on-demand expertise with Agent Skills in Genkit Go]() Original publisher: [Read original article]() Author: Daniela Petruzalek Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Agent Harness](), [context window](), [Go](), [Script]() Tags: [agent](), [agent-skills](), [architecture](), [context-window](), [developers](), [go](), [skills](), [workflows]() ### AI overview This article explains how Agent Skills in Genkit Go provide on-demand specialized expertise through progressive disclosure. Skills package instructions, references, and scripts in modular SKILL.md bundles, exposing only metadata initially and loading full content when a task requires it. ### Source excerpt To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed. ## A better way to build MCP servers with Laravel DevFeed: [A better way to build MCP servers with Laravel]() Original publisher: [Read original article]() Author: Pushpak Chhajed Published: 2026-09-11T12:01:00Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [MCP](), [Laravel](), [MCP Server](), [context window](), [JSON Schema](), [Cloudflare]() Tags: [cloudflare](), [context-window](), [json-schema](), [laravel](), [mcp](), [mcp-server]() ### AI overview The article explains how Laravel MCP 1.0 uses searchable tool catalogs to avoid loading every MCP tool definition into an agent's context on every request. It describes the context costs of large tool surfaces and the evolution of the author's implementation. ### Source excerpt Every MCP tool loads on every request. Laravel MCP 1.0 fixes that with searchable tool catalogs: the same payload for 10 or 100 tools. ## Ling 3.0 Flash Sante is now available on AI Gateway for free DevFeed: [Ling 3.0 Flash Sante is now available on AI Gateway for free]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-04T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [context window]() Tags: [agentic](), [ai](), [ai-gateway](), [coding](), [context-window](), [free](), [healthcare](), [model](), [retrieval]() ### AI overview Ling 3.0 Flash Sante, a healthcare-focused model from inclusionAI, is available on AI Gateway for free through October 4. It supports medical reasoning, research, evidence retrieval, multi-step healthcare tasks, function calling, and a 256K-token context window. ### Source excerpt Ling 3.0 Flash Sante from inclusionAI is now available on AI Gateway, and free through October 4. Ling 3.0 Flash Sante is the healthcare-focused version of Ling 3.0 Flash. It's built for medical reasoning, research, evidence retrieval, and multi-step healthcare tasks. It has a 256K-token context window, supports function calling, and retains the base model's general reasoning, coding, and agentic capabilities. Choose a model ID based on what should happen after the free period: inclusionai/ling-3.0-flash-sante continues serving requests at standard rates after October 4. inclusionai/ling-3.0-flash-sante-free stops serving requests after October 4. Free requests remain visible in your usage and traces but show a cost of $0. Use the model: Try Ling 3.0 Flash Sante in the model playground, or see the free model page. Read more ## Gemini 3.8 Flash now available on AI Gateway DevFeed: [Gemini 3.8 Flash now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-02T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Language models](), [Google](), [context window](), [AI-assisted coding]() Tags: [ai](), [ai-gateway](), [coding-agents](), [context-window](), [flash](), [google](), [inference](), [language-models]() ### AI overview Vercel announces that Gemini 3.8 Flash from Google is available on AI Gateway, with a 1M-token context window, multimodal input, tool calling, web search, and a 65,536-token maximum output. The model is discounted by 50% through December 31 and is positioned as an improvement for software engineering, agent work, and multi-step reasoning. ### Source excerpt Gemini 3.8 Flash from Google is now available on AI Gateway. The model is 50% off through December 31st. It has a 1M token context window, accepts text, image, PDF, and video input, returns text, and supports tool calling and web search. Maximum output is 65,536 tokens. Gemini 3.8 Flash improves on prior Flash models at software engineering, agent work, and multi-step reasoning, at the same speed and cost as the previous release. Thinking is on by default. To use Gemini 3.8 Flash, set model to google/gemini-3.8-flash: To use it in a coding agent, see the coding agents guide, then run vercel ai-gateway coding-agents setup to connect agents like Claude Code, OpenCode, Cursor, Pi, and more and select google/gemini-3.8-flash inside the agent. Try Gemini 3.8 Flash in the model playground. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all language models available on AI Gateway. Read more ## Using Six Scoped Subagents to Manage Context Windows DevFeed: [Using Six Scoped Subagents to Manage Context Windows]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-09-01T05:00:29Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [long-context](), [Artificial Intelligence](), [Code](), [coding](), [Claude](), [Claude Code](), [codex](), [LangChain](), [Python](), [Cloud]() Tags: [agent](), [agents](), [ai](), [claude](), [claude-code](), [cloud](), [codex](), [coding](), [coding-agents](), [context-engineering](), [context-window](), [langchain](), [python]() ### AI overview This tutorial explains how splitting research work among six parallel subagents can reduce context-window noise and limit the state returned to an orchestrator agent. It also covers designing subagent protocols, maintaining an agent registry, and running parallel agent workflows. ### Source excerpt The harness determines what your orchestrator agent never sees, not the model. ## \[Hands-on\] Turn Scientific Figures Into Structured Data with Mistral OCR DevFeed: [\[Hands-on\] Turn Scientific Figures Into Structured Data with Mistral OCR]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-26T21:06:26Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Artificial Intelligence](), [Routing (disambiguation)](), [incident](), [context window](), [FIRST](), [Google](), [SDKs]() Tags: [code](), [mistral](), [ocr]() ### AI overview The supplied excerpts discuss coordinating multiple agents and people through shared channels. They describe the limits of role-based handoffs, including duplicated work, lost context, and invisible negative results, and present Switch as a way to preserve shared reports and threads. The title and body appear to describe different subjects. ### Source excerpt A full walkthrough of the extraction schema, with code. ## Why AI coding agents need context graphs DevFeed: [Why AI coding agents need context graphs]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-08-25T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [AI-assisted coding](), [Graphs](), [coding](), [API](), [internal developer platform](), [context window](), [long-context]() Tags: [ai-coding-agents](), [api-governance](), [apis](), [context-window](), [developer](), [developer-platform](), [engineering](), [general](), [graphs](), [knowledge-graphs]() ### AI overview The article argues that AI coding agents struggle in real codebases primarily because they lack access to a knowledge graph connecting code, APIs, services, ownership, dependencies, policies, and related organizational context. It describes service catalogs, API registries, ownership maps, dependency graphs, and internal developer platforms as different forms of the same underlying structure. It also argues that simply increasing the context window does not solve retrieval and context-quality problems. ### Source excerpt AI coding agents don't fail from small context windows. They fail without a knowledge graph of your code, APIs, and vendors. The post Why AI coding agents need context graphs appeared first on Postman Blog. ## Context Engineering for Coding Agents DevFeed: [Context Engineering for Coding Agents]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-25T05:01:37Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [AI-assisted coding](), [coding](), [Claude](), [codex]() Tags: [agents](), [claude](), [codex](), [coding-agents](), [context](), [context-engineering](), [context-window](), [harness]() ### AI overview This tutorial explains context engineering for coding agents, focusing on memory, skills, LSP servers, compaction, and feedback loops. It presents these techniques as ways to keep an agent's context high-signal and improve coding-agent performance. ### Source excerpt The 4 harness components that keep your context window high-signal. ## Thinking Machines' Inkling: Architecture and Customization Choices DevFeed: [Thinking Machines' Inkling: Architecture and Customization Choices]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-18T15:30:36Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Artificial Intelligence](), [Mixture of Experts (MoE)](), [context window](), [Fine-tuning](), [interfaces](), [data]() Tags: [ai](), [architecture](), [context-window](), [fine-tuning](), [hugging-face](), [mixture-of-experts](), [model](), [real-time]() ### AI overview The article examines the architecture and design choices behind Thinking Machines' Inkling model, including its mixture-of-experts structure, local and global attention, position encoding, multimodal inputs, and adjustable thinking effort. It also notes that Inkling is the company's first model trained from scratch and that its weights are available on Hugging Face under an Apache 2.0 license. ### Source excerpt In this article, we will work through the various choices Thinking Machines made while building Inkling. ## Configuring compaction thresholds and context windows for coding agents DevFeed: [Configuring compaction thresholds and context windows for coding agents]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-14T19:33:33Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [coding](), [context window](), [long-context]() Tags: [agent](), [coding](), [context-window](), [generation](), [model](), [recovery](), [tokens]() ### AI overview The article explains how to derive a coding agent's effective context window from compaction thresholds and the response runway required to complete generation. It discusses threshold behavior, model metadata, route limits, generation clamping, overflow detection, and recovery. ### Source excerpt Derive your coding agent's effective context window from two numbers: the compaction threshold you want, and the response runway the model needs to finish. ## Claude Code statusline: model name and context usage at a glance DevFeed: [Claude Code statusline: model name and context usage at a glance]() Original publisher: [Read original article]() Author: Ivan Morgillo Published: 2026-08-11T10:00:00Z Content type: tutorial Language: en Sources: [Ivan Morgillo]() Topics: [Claude Code](), [context window](), [JSON](), [Terminal](), [Code]() Tags: [claude-code](), [code](), [context-window](), [json](), [terminal]() ### AI overview A tutorial on configuring a Claude Code terminal statusline that reads the JSON payload supplied to a statusline script. The resulting bar displays the project and branch, current model, and color-coded context usage with token counts. ### Source excerpt Claude Code pipes a JSON payload into your statusline script. Read it, and the bar shows the project and branch you are on, the model, and color-coded context usage with token counts. ## Context Layer for AI SDLC: What It Is and How to Build Yours DevFeed: [Context Layer for AI SDLC: What It Is and How to Build Yours]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:34:08Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [AI Agent](), [sdlc](), [context window](), [API](), [GitHub API](), [GitHub](), [Security]() Tags: [ai](), [ai-agent](), [api](), [context-window](), [cost](), [engineering](), [github](), [how-to](), [sdlc](), [security]() ### AI overview This article explains context layers for AI-driven software development. A context layer consolidates continuously updated information about services, ownership, dependencies, runtime state, permissions, and organizational relationships so agents can query one accurate model instead of repeatedly combining data from separate tools and APIs. It discusses what to include, whether to build or buy, possible uses, return on investment, measurement, and implementation, while reporting an 80% reduction in per-query agent cost in Port's measurements. ### Source excerpt Learn what a context layer is, what goes into one, whether to build or buy one, and how it cuts AI agent costs across the SDLC. ## GPT-5.6 Luna and Fable 5 Compared in a Bug-Fixing Cost Benchmark DevFeed: [GPT-5.6 Luna and Fable 5 Compared in a Bug-Fixing Cost Benchmark]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-08-02T12:24:55Z Content type: opinion Language: en Sources: [The Product Compass]() Topics: [Artificial Intelligence](), [Benchmark](), [anthropic](), [Claude](), [OpenAI](), [ChatGPT](), [API](), [Claude Code](), [context window](), [coding]() Tags: [ai](), [api](), [benchmark](), [chatgpt](), [claude](), [claude-code](), [efficiency](), [performance](), [token]() ### AI overview The article describes experiments comparing AI model performance and cost for bug fixing. In the author's benchmark, GPT-5.6 Luna at maximum reasoning effort fixed 33 of 105 hidden bugs at an API cost of $1.80, while Fable 5 fixed 29 at a cost of $104. ### Source excerpt Measured, not estimated: 105 hidden bugs, 10 frontier models, 14 runs. The $1.80 run beat the $104 one. My routing, and the setup. ## We're Shipping Faster While Understanding Less DevFeed: [We're Shipping Faster While Understanding Less]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-07-30T12:04:26Z Content type: opinion Language: en Sources: [Eric Roby]() Topics: [AI-assisted coding](), [Code review](), [Software](), [context window]() Tags: [ai-coding-agents](), [coding-agents](), [context-window](), [review](), [software]() ### AI overview This opinion argues that AI coding agents are increasing the volume and speed of software development while creating comprehension debt. Developers may understand less of the code they ship because reviewing AI-generated changes takes longer and can become a bottleneck. The article argues that developers must deliberately build understanding through review and attention to how systems fit together. ### Source excerpt AI Writes Most of Your Code Now. The Hard Part Is Still Yours. ## Why AI Agents need a Context Lake DevFeed: [Why AI Agents need a Context Lake]() Original publisher: [Read original article]() Author: Matan Grady Published: 2026-07-30T11:27:22Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [Model Context Protocol](), [context window](), [Security](), [Latency]() Tags: [ai](), [ai-agents](), [claude-code](), [context-window](), [cursor](), [github-copilot](), [latency](), [mcp](), [security]() ### AI overview The article explains that scaling AI agents across an organization is hindered by security approvals, excessive MCP tool definitions that inflate context-window usage and latency, and insufficient organizational context for answering operational questions accurately. It argues that agents need a shared context lake to understand company terminology, ownership, repositories, pull requests, and deployment information. ### Source excerpt Discover why AI agents need a context lake to operate effectively, enabling better decision-making and system-wide intelligence. ## Research: How we cut AI costs by 80% DevFeed: [Research: How we cut AI costs by 80%]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-30T11:24:23Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [context window](), [MCP](), [semantic-layer](), [Claude](), [data](), [sdlc](), [GitHub](), [Caching]() Tags: [agents](), [ai](), [claude](), [context-window](), [cost](), [data](), [experiment](), [github](), [mcp](), [reasoning](), [sdlc](), [semantic-layer](), [tokens]() ### AI overview This research article examines rising AI costs caused by messy, repeatedly assembled context for agent queries. An experiment using thousands of production queries and a test set of 1000 commonly asked SDLC queries found that structured context was 80% cheaper than unstructured context. The proposed approach pre-relates context and uses a semantic layer to reduce data hops, reasoning, and token consumption. ### Source excerpt We ran thousands of AI queries on unstructured and structured context and measured the cost. Structured context was 80% cheaper than unstructured. ## Laguna S 2.1 is now available on AI Gateway DevFeed: [Laguna S 2.1 is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [agentic-coding](), [Mixture of Experts (MoE)](), [context window](), [API](), [MLOps](), [AI Research](), [API keys](), [Routing (disambiguation)]() Tags: [agentic-coding](), [ai-models](), [ai-research](), [api-keys](), [context-window](), [cost](), [mixture-of-experts](), [mlops](), [routing](), [sdk](), [support]() ### AI overview Poolside's Laguna S 2.1 is now available through Vercel AI Gateway in free and paid versions, with context windows of 256K and 1M tokens. The open-weight Mixture-of-Experts model supports thinking and no-thinking modes and is designed for agentic coding, long-running tasks, browser tooling, MLOps pipelines, and AI research. ### Source excerpt Laguna S 2.1 from Poolside is now available on AI Gateway. There are 2 versions of the model available: Free version (256K context window): poolside/laguna-s-2.1-free Paid version (1M context window): poolside/laguna-s-2.1 Laguna S 2.1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes. The model specializes in agentic coding and long-running tasks, including writing and debugging code, running tests, building browser-based tooling, and working on MLOps pipelines and AI research. In thinking mode, Laguna S 2.1 reports 70.2% on Terminal-Bench 2.1, 78.5% on SWE-bench Multilingual, and 59.4% on SWE-Bench Pro. To use Laguna S 2.1, set model to poolside/laguna-s-2.1-free or poolside/laguna-s-2.1 in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Laguna S 2.1 in the model playground. Read more ## A Feedback-Driven Agent Pattern for Self-Repairing AI Workflows DevFeed: [A Feedback-Driven Agent Pattern for Self-Repairing AI Workflows]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-07-16T06:17:34Z Content type: tutorial Language: en Sources: [Wix Engineering]() Topics: [Loop Engineering](), [AI Agent](), [Code](), [context window](), [integrity]() Tags: [agentic-workflows](), [agents](), [ai-agent](), [ai-agents](), [code](), [context-window](), [validation]() ### AI overview This tutorial proposes a Feedback-Driven Agent pattern for building self-repairing AI agent workflows. It separates deterministic structural validation from goal-based assessment: agents produce artifacts, programmatic checks identify failures, and structured error reports route the agent toward repair. ### Source excerpt Every developer building with AI agents eventually hits the same wall. You write a masterpiece of a system prompt, loaded with rules, edge cases, and brand guidelines. It works perfectly... until you add one more rule. Suddenly, the agent gets "context fatigue," ignores half your instructions, and hallucinates a creative but entirely broken solution. We need to stop trying to make agents perfect on the first try. Instead, we suggest a new architectural approach: organizing agentic workflows... [Next page]()