# agentic workflows Published articles for agentic workflows. 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 to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## GraphQL's Maturity and Readiness for Production and Agentic Workflows DevFeed: [GraphQL's Maturity and Readiness for Production and Agentic Workflows]() Original publisher: [Read original article]() Author: Jeff Auriemma Published: 2026-07-29T14:02:52Z Content type: opinion Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [Orchestration](), [agentic workflows](), [AI Agent]() Tags: [2026](), [agentic-workflows](), [ai-agents](), [announcement](), [enterprise](), [gartner](), [graphql](), [linux-foundation](), [orchestration](), [standards]() ### AI overview The article argues that GraphQL's placement in Gartner's Trough of Disillusionment reflects a useful shift from hype toward evaluating production readiness. It says GraphQL's existing enterprise adoption may better prepare companies to deploy AI agents, while forecasting that GraphQL will reach the Plateau of Productivity in less than two years. ### Source excerpt American Airlines built its API orchestration layer before AI agents existed, then used it as the agent layer. See why using GraphQL is the real head start. ## 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... ## How GitHub improved Copilot code review by reshaping agent workflows DevFeed: [How GitHub improved Copilot code review by reshaping agent workflows]() Original publisher: [Read original article]() Author: Napalys Klicius Published: 2026-07-10T15:57:47Z Content type: article Language: en Sources: [GitHub]() Topics: [GitHub Copilot code review](), [Code review](), [Pull Request](), [GitHub](), [GitHub Copilot](), [Command-line interface](), [Unix]() Tags: [agentic-coding](), [agentic-workflows](), [ai-ml](), [architecture-optimization](), [cli](), [code-review](), [copilot](), [developer-tools](), [engineering](), [github](), [github-copilot](), [github-copilot-code-review](), [llms](), [pull-request](), [review](), [unix](), [workflows]() ### AI overview GitHub describes how migrating Copilot code review to shared Unix-style exploration tools initially increased review costs and reduced issue detection in benchmarks. Rewriting the reviewer instructions around pull request workflows led to roughly 20% lower average review cost while maintaining the same review quality. ### Source excerpt How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence. The post Better tools made Copilot code review worse. Here's how we actually improved it. appeared first on The GitHub Blog. ## Automating cross-repo documentation with GitHub Agentic Workflows DevFeed: [Automating cross-repo documentation with GitHub Agentic Workflows]() Original publisher: [Read original article]() Author: David Pine Published: 2026-07-08T21:11:56Z Content type: article Language: en Sources: [GitHub]() Topics: [GitHub](), [GitHub Actions](), [pull-requests](), [Documentation](), [Automation]() Tags: [agentic-workflows](), [ai-ml](), [architecture-optimization](), [automation](), [dev-tools](), [devops](), [documentation](), [engineering](), [generative-ai](), [github](), [github-agentic-workflows](), [github-copilot](), [llms](), [machine-learning](), [pull-requests]() ### AI overview The Aspire team describes using GitHub Agentic Workflows to automate documentation across separate product and documentation repositories. The workflow turns merged product changes into documentation pull requests that are reviewed by the engineer who shipped the feature; the article reports 82 such pull requests for Aspire 13.3 and 13.4, with a median delay of 44.8 hours after the related product pull request. ### Source excerpt Explore how the Aspire team turns merged product changes into SME-reviewed docs pull requests, closing the gap between release and documentation. The post Automating cross-repo documentation with GitHub Agentic Workflows appeared first on The GitHub Blog. ## Improving the speed and energy-efficiency of AI agents DevFeed: [Improving the speed and energy-efficiency of AI agents]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-25T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [agentic workflows](), [Loop Engineering](), [Green Software](), [Cloud](), [Microsoft](), [microsoft-azure]() Tags: [adam-belay](), [agentic-workflows](), [agents](), [ai](), [ai-agents](), [algorithms](), [artificial-intelligence](), [cloud](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [data](), [data-centers](), [defense-advanced-research-projects-agency-darpa](), [deployment](), [efficiency](), [electrical-engineering-and-computer-science-eecs](), [electronics](), [energy](), [energy-efficiency](), [gohar-chaudhry](), [machine-learning](), [microsoft](), [microsoft-azure](), [mit-schwarzman-college-of-computing](), [murakkab](), [research](), [school-of-engineering](), [software](), [sustainability](), [sustainable-computing](), [workflows]() ### AI overview MIT and Microsoft researchers developed Murakkab, a system that automatically designs and deploys agentic workflows by selecting models, tools, hardware configurations, and computational resources according to user priorities. Tests found that it reduced computational requirements, energy use, and costs without reducing performance. ### Source excerpt A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications. ## Codex wants to work on more than just code DevFeed: [Codex wants to work on more than just code]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-06-11T11:44:06Z Content type: opinion Language: en Sources: [The AI-Augmented Engineer]() Topics: [codex](), [Artificial Intelligence](), [ChatGPT](), [MCP](), [agentic workflows](), [Claude Code]() Tags: [agentic-workflows](), [ai](), [chatgpt](), [claude-code](), [codex](), [mcp]() ### AI overview This opinion article examines OpenAI Codex's expansion from a coding assistant into a broader work assistant. It discusses integrations with Gmail, Slack, Google Drive, Linear, and Stripe, the role of the open MCP standard, and OpenAI's plan to bring Codex into ChatGPT. The author questions whether this broader direction is wise. ### Source excerpt My favorite AI assistant for code is making the leap into becoming the AI assistant for work. I mean, take a look at the landing page as of today. ## AI as the Next Abstraction Layer: How I see engineering evolving at Thumbtack DevFeed: [AI as the Next Abstraction Layer: How I see engineering evolving at Thumbtack]() Original publisher: [Read original article]() Author: Ananda Kanagaraj Sankar Published: 2026-05-22T23:53:03Z Content type: opinion Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Artificial Intelligence](), [agentic workflows](), [Development](), [Software Engineering](), [engineering-leadership]() Tags: [agentic-workflows](), [ai](), [ai-agent](), [chatgpt](), [copilot](), [development](), [engineering](), [engineering-culture](), [generative-ai-tools](), [software-engineering]() ### AI overview A personal perspective on how AI-assisted development is evolving at Thumbtack, from informal experimentation with ChatGPT and Copilot to end-to-end agentic workflows. The article frames AI as a new abstraction layer and highlights its non-determinism as a major difference from earlier abstractions. ### Source excerpt Over the past year, the way we use AI at Thumbtack has gone through a few phases. Early on it was mostly curiosity, people experimenting with ChatGPT and Copilot on side projects, sharing tips in Slack. Then the models got noticeably better at working inside real, mature codebases (not just greenfield projects) and the conversation shifted. It stopped being about whether we should adopt AI-assisted development and became about how. Lately, it is moving towards the adoption of end to end agentic workflows for development. I've been thinking a lot about what this shift means, not just for our codebase or our velocity, but how it impacts what it actually feels like to be an engineer here. What follows is my personal perspective, shaped by leading engineering on our monetization teams and informed by how Thumbtack's engineering leadership has been approaching this across teams. Another layer of abstraction If you zoom out, software engineering has always been a story of rising abstraction. We went from assembly to C, from C to Java, from hand-rolled servers to cloud-native infrastructure. And each time, the craft shifted rather than shrinking. For example, managing memory manually was replaced by designing distributed systems. I think AI-assisted development is the next version of that pattern.The difference this time isn't that the new layer takes on implementation work. Compilers always did some of that, with their own undefined behavior and implementation-defined choices. The difference is the leap in non-determinism. The earlier abstractions were designed to be mostly deterministic and mostly non-leaky, and AI tools break that pattern. The same prompt produces different code on different days, with different trade-offs and different bugs -- that's a bigger shift than swapping languages. I keep coming back to the same historical pattern: the engineers who thrive are the ones who can operate at the new layer, not the ones who insist on staying anchored to the old one. ## 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 ## The Figma design agent is here DevFeed: [The Figma design agent is here]() Original publisher: [Read original article]() Author: Rodrigo Davies; Tammy Taabassum Published: 2026-05-20T12:00:00Z Content type: release Language: en Sources: [Figma Blog]() Topics: [Figma](), [Figma agent](), [Artificial Intelligence](), [Design system](), [MCP](), [Code]() Tags: [agentic](), [agentic-workflows](), [ai](), [design-system](), [figma](), [figma-agent](), [mcp](), [tokens](), [tools](), [workflow](), [workflows]() ### AI overview Figma announces a purpose-built Figma agent that works directly on the canvas and in the left rail. The agent uses context about components, tokens, standards, and best practices to help teams explore ideas, edit files, and iterate within shared design workflows. ### Source excerpt Starting today, work with an agent that is built for Figma--directly on the canvas. ## Temporal Sandbox Orchestration Harness: The missing layer for running agents DevFeed: [Temporal Sandbox Orchestration Harness: The missing layer for running agents]() Original publisher: [Read original article]() Author: Stefan Richter Published: 2026-05-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [agentic workflows](), [Persistence](), [Provisioning]() Tags: [agentic-workflows](), [agents](), [ai-agents](), [cleanup](), [durability](), [execution](), [files](), [harness](), [isolation](), [network](), [orchestration](), [persistence](), [process](), [product-news](), [provisioning](), [sandbox](), [temporal]() ### AI overview Temporal introduces reference materials and Code Exchange samples for orchestrating secure, isolated, temporary sandbox compute environments within Temporal Workflows used by AI agents. The materials address interfaces, provisioning, persistence, and cleanup. ### Source excerpt Standardize how AI agents in Temporal Workflows orchestrate sandbox compute. New Code Exchange samples cover provisioning, persistence, and cleanup. ## Soldøgn Interop Recap ☀ DevFeed: [Soldøgn Interop Recap ☀]() Original publisher: [Read original article]() Author: Tim Beiko Published: 2026-05-02T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Network](), [benchmarking](), [Code](), [MCP]() Tags: [agentic-workflows](), [benchmarking](), [contributors](), [ethereum](), [mcp-server](), [network](), [recap](), [research-development](), [upgrade]() ### AI overview A recap of Soldøgn Interop, where more than 100 Ethereum core contributors worked in Svalbard on hardening the Glamsterdam network upgrade. The week produced agreement on a post-upgrade gas limit floor of 200M, stable ePBS implementations with external builders, finalized EIP-8037 repricing numbers, and progress on related features. ### Source excerpt This past week, just over 100 Ethereum core contributors gathered above the Arctic Circle -- in Longyearbyen, Svalbard -- for the Soldøgn Interop: a week of intense work on the Glamsterdam network upgrade. Soldøgn followed last year's Berlinterop, but returned to the format used by Amphora 🏺, [Edelweiss 🏔](https://blog.ethereum.org/2023/02/07/edelweiss-interop-recap... ## How to Prevent Prompt Injection in AI Agents DevFeed: [How to Prevent Prompt Injection in AI Agents]() Original publisher: [Read original article]() Author: sam.nawab@goteleport.com (Sam Nawab) Published: 2026-02-09T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [prompt injection](), [agentic workflows](), [Artificial Intelligence](), [Large Language Model](), [CI/CD]() Tags: [agentic-workflows](), [agents](), [ai](), [ai-agents](), [ci-cd](), [infrastructure](), [permissions](), [prompt-injection](), [vulnerability]() ### AI overview This article explains how prompt injection affects AI agents that combine system prompts, retrieved context, user input, and tool-related inputs. It describes how manipulated inputs can influence tool calls, API calls, deployment targets, and operational decisions in connected infrastructure, and discusses controls for limiting the impact. ### Source excerpt Prevent prompt injection in AI agents by understanding how agentic workflows affect infrastructure and what controls help contain blast radius. ## AI Engineers Should Focus on Systems, Architecture, and Production in 2026 DevFeed: [AI Engineers Should Focus on Systems, Architecture, and Production in 2026]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-01-13T11:03:06Z Content type: opinion Language: en Sources: [Neural Bits]() Topics: [Artificial Intelligence](), [systems](), [agentic workflows](), [Inference](), [AI Strategy]() Tags: [2025](), [2026](), [agentic-workflows](), [ai](), [ai-engineer](), [architecture](), [compute](), [inference](), [production](), [reports](), [research]() ### AI overview This opinion article argues that AI engineers should prioritize system design, architecture, scale, inference, monitoring, testing, and core engineering fundamentals in 2026. It says most organizations remain in research or experimentation, and that production failures often stem from the engineering around AI models rather than the models themselves. ### Source excerpt A no-BS breakdown of where to invest your time, backed by real industry insights. ## Gemini 3 Flash: frontier intelligence built for speed DevFeed: [Gemini 3 Flash: frontier intelligence built for speed]() Original publisher: [Read original article]() Author: Tulsee Doshi Published: 2025-12-17T11:58:17Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [agentic workflows](), [multimodal](), [API](), [Code](), [Google](), [google-antigravity]() Tags: [agentic](), [agentic-workflows](), [ai](), [ai-studio](), [antigravity](), [api](), [benchmarks](), [cost](), [development](), [efficiency](), [gemini](), [google-ai](), [google-antigravity](), [latency](), [none](), [performance](), [platform](), [reasoning](), [release](), [speed](), [tokens]() ### AI overview Google announces Gemini 3 Flash, a faster and more efficient model in the Gemini 3 family. The release targets developers, consumers, and enterprises across Google's platforms, with support for reasoning, multimodal understanding, and agentic workflows. ### Source excerpt Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost. ## Introducing GPT-5.2 DevFeed: [Introducing GPT-5.2]() Original publisher: [Read original article]() Published: 2025-12-11T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Frontier Model](), [OpenAI](), [ChatGPT](), [Artificial Intelligence](), [API](), [agentic-coding](), [long-context](), [coding](), [Tool](), [Data Science](), [data]() Tags: [agentic](), [agentic-coding](), [agentic-workflows](), [ai](), [analysis](), [api](), [benchmarks](), [bug](), [chatgpt](), [code-reviews](), [coding](), [context](), [data](), [frontier-model](), [gpt](), [long-context](), [model](), [openai](), [product](), [reasoning](), [vision]() ### AI overview OpenAI introduces GPT-5.2, a frontier model series designed for professional knowledge work. It improves coding, vision, long-context understanding, tool use, reasoning, document analysis, data science, and complex multi-step projects, with availability in ChatGPT and the API. ### Source excerpt GPT-5.2 is our most advanced frontier model for everyday professional work, with state-of-the-art reasoning, long-context understanding, coding, and vision. Use it in ChatGPT and the OpenAI API to power faster, more reliable agentic workflows. ## Temporal and the next frontier: Scaling AI reliably DevFeed: [Temporal and the next frontier: Scaling AI reliably]() Original publisher: [Read original article]() Author: Samar Abbas Published: 2025-09-03T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [systems](), [AI Infrastructure](), [agentic workflows](), [SDKs](), [OpenAI](), [MongoDB](), [Pydantic](), [Retrieval Augmented Generation (RAG)]() Tags: [agentic-workflows](), [ai](), [ai-infrastructure](), [announcements](), [context-engineering](), [developer-community](), [mongodb](), [openai](), [rag](), [sdk]() ### AI overview Temporal describes how its durable workflow infrastructure is being applied to production AI systems, including agentic workflows, multiple AI models, stateful processes, and integrations with OpenAI, MongoDB, and Pydantic. ### Source excerpt At Temporal, we've always focused on one thing: making it simpler to build systems that work and keep working -- reliably at scale and in the messy reality of prod. That mission matters more today than ever. ## From AI hype to durable reality -- why agentic flows need distributed-systems discipline DevFeed: [From AI hype to durable reality -- why agentic flows need distributed-systems discipline]() Original publisher: [Read original article]() Author: Kevin Martin Published: 2025-06-11T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [agentic workflows](), [Model Context Protocol](), [Artificial Intelligence]() Tags: [agentic-workflows](), [ai](), [distributed](), [distributed-systems](), [mcp](), [temporal-voices]() ### AI overview The article explains how Temporal's Durable Execution can support resilient agentic workflows and MCP tools. It describes a weather-agent example in which MCP tools are wrapped in Temporal Workflows and an HTTP call is implemented as a Temporal Activity, arguing that production AI systems face distributed-systems challenges. ### Source excerpt Learn how Temporal's Durable Execution powers resilient AI agents and MCP tools, turning agentic workflows into scalable, crash-proof production systems. ## Temporal use case roundup: Generative AI DevFeed: [Temporal use case roundup: Generative AI]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2024-11-19T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Generative AI](), [AI Development](), [agentic workflows](), [Orchestration](), [Programming](), [Conversational AI](), [Synthetic Data Generation](), [data-processing]() Tags: [agentic](), [agentic-workflows](), [ai](), [applications](), [conversational-ai](), [datasets](), [generative-ai](), [processing](), [temporal-voices](), [video](), [workflows]() ### AI overview This use-case roundup describes how Temporal orchestrates reliable, long-running workflows for generative AI applications. It covers video processing and translation, conversational AI and call transcription, synthetic data generation, predictive analytics, and cryptocurrency data enrichment. ### Source excerpt See how Temporal boosts generative AI applications across industries, from video processing to conversational AI, by orchestrating complex, reliable workflows. ## Un Ministral, des Ministraux DevFeed: [Un Ministral, des Ministraux]() Original publisher: [Read original article]() Published: 2024-10-16T02:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Language models](), [Artificial Intelligence](), [Inference](), [Low Latency](), [vllm](), [llama](), [gemma]() Tags: [agentic](), [agentic-workflows](), [ai](), [comparison](), [evaluation](), [function-calling](), [inference](), [language-models](), [low-latency](), [mistral](), [models](), [vllm]() ### AI overview Mistral introduces Ministral 3B and Ministral 8B, compact language models designed for on-device and edge use cases. The models support up to 128k context, with Ministral 8B adding interleaved sliding-window attention for faster, more memory-efficient inference. They target privacy-first, compute-efficient, low-latency applications such as translation, smart assistants, analytics, autonomous robotics, and agentic workflows. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.