# agentic workflow Published articles for agentic workflow. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## GitHub agentic workflows and Renovate DevFeed: [GitHub agentic workflows and Renovate]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-08-02T00:00:00Z Content type: tutorial Language: en Sources: [Nicolas Fränkel]() Topics: [GitHub](), [GitHub Actions]() Tags: [agentic-workflow](), [dependencies](), [github](), [github-actions](), [github-agentic-workflows](), [pull-requests](), [renovate](), [rollback](), [technical]() ### AI overview This article explains how Renovate dependency updates can break GitHub agentic workflows by changing an action SHA without updating the matching generated scripts. It also describes a failed recompilation workflow that rolled back an unrelated Node.js update, then identifies the need to keep the generated lock file synchronized with its release. ### Source excerpt I've been a big fan of Renovate for a couple of years already. Renovate scans your repositories, detects outdated package versions, and opens pull requests to automatically bump them. It's similar to Dependabot in that it keeps your dependencies up to date. If I had to compare them in one sentence, I'd say Renovate is less integrated in the GitHub ecosystem, but handles more ecosystems and, more importantly, is extensible. ## Modernizing sourcing at scale: How intelligent agents are changing procurement DevFeed: [Modernizing sourcing at scale: How intelligent agents are changing procurement]() Original publisher: [Read original article]() Author: Shreya Peddi Published: 2026-07-21T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [microsoft 365](), [Microsoft]() Tags: [agent](), [agentic-workflow](), [agents](), [generative-ai-finance](), [microsoft](), [microsoft-365](), [procurement](), [procurement-strategy](), [setup](), [structured](), [supply-chain](), [tool]() ### AI overview This article describes how a Microsoft procurement professional built the Supplier Price Interpreter with M365 Chat Agent Builder. The agent uses supplier price sheets stored in a structured Teams channel as a knowledge source and responds to plain-language prompts about the data, aiming to reduce manual comparison work in RFP and RFx processes. ### Source excerpt Image generated by AI Picture this: It's late in the week, an urgent sourcing decision is due, and sitting in your inbox are three separate supplier price sheets -- each formatted differently, each with its own tab structure, its own naming conventions, its own quirks. Your job is to compare them, find the gaps, flag the inconsistencies, and deliver a recommendation. The clock is ticking. This isn't a hypothetical scenario. It happens regularly in procurement teams running large scale RFP and RFx processes. For years, the solution was straightforward: Open each file and compare them line by line. It worked, but it was slow, manual, and far removed from the strategic work that sourcing professionals are hired to do. In a world where business decisions move fast and supplier negotiations are time-sensitive, delays in price review have real consequences. Slower cycles, missed windows, and sourcing teams spending their cognitive energy on extraction instead of analysis. So, we asked the question: What if the extraction part could just happen on its own? Starting small, at the desk The Supplier Price Interpreter didn't start as a grand initiative. It started as a practical frustration. Rupa Shastri, a procurement professional within Microsoft's broader procurement organization, built the agent right at her desk using M365 Chat Agent Builder. No dedicated engineering team, no lengthy development cycle, no new infrastructure. Just a clear problem, an available tool, and the willingness to try something different. Figure 1: M365 Copilot flow and the actionable insights it provides. Working within a large enterprise procurement environment, where supplier bids come in across multiple RFx engagements spanning vendors, scopes, and formats, the repetitive nature of price sheet review was hard to ignore. Every engagement required the same foundational steps. Open the files, read the data, compare the numbers, find what's missing. The methodology never changed. Only the suppliers ## Durable Execution enables resilient agentic workflows in procedural code DevFeed: [Durable Execution enables resilient agentic workflows in procedural code]() Original publisher: [Read original article]() Author: Maxim Fateev Published: 2025-08-20T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [agentic workflow](), [Orchestration](), [Persistence](), [distributed-systems](), [Error Handling](), [Code]() Tags: [agentic](), [agentic-workflow](), [agents](), [ai](), [error-handling](), [retries](), [stateful](), [temporal-voices](), [tracing]() ### AI overview This opinion article argues that graph-based frameworks are a poor fit for dynamic AI-agent workflows. It presents Durable Execution as an alternative that lets developers use procedural code while retaining persistence, fault tolerance, incremental execution, error handling, retries, and tracing. ### Source excerpt Graphs break for dynamic AI agents. Durable Execution lets you build resilient, stateful workflows in plain code -- with real error handling, retries, and tracing.