# agentic workflows An AI-driven process in which autonomous agents make decisions, coordinate tasks, and execute complex workflows with minimal human intervention. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Software Engineering in the Probabilistic Era of LLM-Assisted Development DevFeed: [Software Engineering in the Probabilistic Era of LLM-Assisted Development]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-07-09T12:35:59Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [Software Engineering](), [Large Language Model](), [Artificial Intelligence](), [agentic workflows](), [AI Development]() Tags: [agentic](), [ai](), [artificial-intelligence](), [llms](), [quality](), [software-engineering](), [velocity](), [workflows]() ### AI overview The article argues that LLMs and agentic workflows are making software development less deterministic. It proposes managing uncertainty through verification, failure recovery, feedback loops, and processes that preserve both software quality and development velocity. ### Source excerpt Software engineering has entered a probabilistic era. As LLMs replace deterministic workflows with uncertainty, the challenge is no longer eliminating variability; it's building systems that deliver both high quality and high development velocity despite it. ## 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. ## Dodo Digest: Every Pricing Model Gets Tested Eventually DevFeed: [Dodo Digest: Every Pricing Model Gets Tested Eventually]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-06-13T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [anthropic](), [agentic workflows]() Tags: [ai](), [anthropic](), [newsletter](), [payments](), [pricing](), [subscription](), [usage-based-billing]() ### AI overview This commentary examines how rising AI model costs are pushing companies toward usage-based and outcome-based pricing. It argues that these models can make costs unpredictable as customers and businesses scale, and emphasizes predictability as an important principle for software and payments. It also announces Dodo Payments v1.101.0, including subscription payment retries, business proration settings, and improved B2B invoicing. ### Source excerpt Anthropic's new flagship model is far more expensive than previous generations, and the whole AI industry is shifting toward usage- and outcome-based pricing. It sounds fair until invoices grow every time customers succeed. The real edge is predictability. Plus, we shipped Dodo Payments v1.101.0. ## 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. ## Managing Agentic AI Costs at Scale DevFeed: [Managing Agentic AI Costs at Scale]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [AI Agent](), [agentic workflows](), [Claude Code](), [AI Inference]() Tags: [agentic-ai](), [ai-agents](), [claude-code](), [cost](), [engineering](), [inference]() ### AI overview This article examines why production agentic AI can cost substantially more than pilot deployments or standard chatbot use. It argues that total task cost includes planning, context retrieval, tool calls, state management, validation, and retries, and discusses building a business case before costs escalate. ### Source excerpt What do the Uber budget blowout, a 24x token multiplier, and context teach us about building a real business case for AI Agents in production? ## 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. ## 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. ## How Agentic Payments Increase Demands on Payment Infrastructure DevFeed: [How Agentic Payments Increase Demands on Payment Infrastructure]() Original publisher: [Read original article]() Author: Jim Harris Published: 2026-02-20T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [agentic workflows](), [Artificial Intelligence](), [Authorization](), [Availability](), [Back end](), [CockroachDB]() Tags: [agentic](), [ai](), [authorization](), [availability](), [backend](), [cockroachdb](), [payments]() ### AI overview The article argues that agentic commerce will place greater demands on payment infrastructure. Concurrent agents, aggressive retries and automated purchasing can expose race conditions and partial failures, making authorization, risk controls, availability and ledger-grade guarantees increasingly important. ### Source excerpt If you own payments infrastructure, platform stability, or risk controls at a payments company, this article's title is not a rhetorical question. ## 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. ## 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. ## pyghidra-mcp: Headless Ghidra MCP Server for Project-Wide, Multi-Binary Analysis DevFeed: [pyghidra-mcp: Headless Ghidra MCP Server for Project-Wide, Multi-Binary Analysis]() Original publisher: [Read original article]() Author: clearbluejar Published: 2025-08-19T15:56:00Z Content type: release Language: en Sources: [clearbluejar]() Topics: [Ghidra](), [Reverse Engineering](), [MCP Server](), [Model Context Protocol](), [Large Language Model](), [Artificial Intelligence](), [Automation](), [agentic workflows]() Tags: [agentic](), [automated](), [engineering](), [ghidra](), [llm](), [mcp](), [mcp-server](), [reverse-engineering]() ### AI overview The article introduces pyghidra-mcp, a headless Model Context Protocol server for Ghidra. It is designed for automation and exposes an entire Ghidra project so an LLM can trace function calls across multiple interdependent binaries in one analysis session. ### Source excerpt Unlock project-wide, multi-binary analysis with pyghidra-mcp, a headless Ghidra MCP server for automated, LLM-assisted reverse engineering. ## 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.