# Requirements Software requirements is the discipline of identifying, documenting, and managing the conditions and capabilities that software systems must satisfy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why human oversight is shifting from writing code to defining requirements DevFeed: [Why human oversight is shifting from writing code to defining requirements]() Original publisher: [Read original article]() Author: Naseeb Ahmed Mian Published: 2026-09-17T13:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [Requirements](), [AI Agent](), [Code](), [Availability]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [andela](), [automated](), [availability](), [code](), [pipeline](), [post-contributed](), [requirements](), [review](), [software-development](), [sponsor-andela](), [sponsored-post-contributed](), [tests]() ### AI overview The article argues that human oversight of AI-assisted software development should focus on defining and reviewing requirements, not only checking whether generated code conforms to them. It illustrates the risk with a flawed availability-related requirement that passed specification review, generated six passing tests, traceability checks, and automated QA while violating the feature's intended outcome. ### Source excerpt This walks through the pipeline our agents operate inside--from a recorded scoping meeting through unit specs, spec review, generated code, The post Why human oversight is shifting from writing code to defining requirements appeared first on The New Stack. ## KYC Onboarding: From Signup to Accepting Payments DevFeed: [KYC Onboarding: From Signup to Accepting Payments]() Original publisher: [Read original article]() Author: Deepak Jangir Published: 2026-09-17T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Requirements](), [Security, Privacy and Abuse Prevention](), [account](), [Website]() Tags: [account](), [business](), [credentials](), [guide](), [onboarding](), [payments](), [saas](), [verification](), [website]() ### AI overview This guide explains KYC onboarding for online sellers, including the identity, eligibility, ownership, bank account, and website checks payment providers may perform before enabling payments and payouts. It outlines the onboarding sequence, common causes of delays, and differences between freelancers and companies. ### Source excerpt What KYC onboarding involves when you sign up to sell online: the stages, the documents, why applications stall, and how it differs for freelancers and companies. ## Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers DevFeed: [Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers]() Original publisher: [Read original article]() Author: Josh Parker Published: 2026-09-16T13:00:33Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [data centers](), [AI Infrastructure](), [Google](), [Nvidia](), [Artificial Intelligence](), [datacenter](), [benchmarking](), [Requirements]() Tags: [ai](), [ai-data-centers](), [ai-infrastructure](), [capacity](), [corporate](), [data-centers](), [energy](), [google](), [infrastructure](), [launch](), [metrics](), [nvidia](), [performance-metrics](), [requirements](), [resource](), [systems]() ### AI overview Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance, a coalition focused on flexible AI data centers that can dynamically adjust electricity use in response to grid conditions. The article describes technology-neutral, performance-based requirements covering response speed, duration, predictability and emergency behavior. ### Source excerpt AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use [...] ## Using acceptance tests to catch regression bugs in legacy code before production DevFeed: [Using acceptance tests to catch regression bugs in legacy code before production]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-09-16T07:33:58Z Content type: tutorial Language: en Sources: [Optivem Journal]() Topics: [Testing](), [Requirements](), [legacy]() Tags: [automated](), [bugs](), [e2e](), [legacy-code](), [regression](), [requirements](), [testing](), [ui](), [unit-tests]() ### AI overview This hands-on article explains why automated unit and end-to-end tests may still allow production bugs in legacy code. It presents acceptance test-driven development (ATDD), where requirements and tests describe the same observable behavior, making coverage clearer and reducing dependence on UI steps. ### Source excerpt You automated, and manual regression testing is still there. Those two facts have the same cause. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## Bullshit Management Didn't Die. It Got Automated. DevFeed: [Bullshit Management Didn't Die. It Got Automated.]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-09-03T12:27:11Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Product Management](), [Requirements](), [Artificial Intelligence](), [Large Language Model](), [User Stories]() Tags: [2026](), [generate](), [llms](), [product-management](), [requirements](), [user-stories]() ### AI overview The author revisits the concept of "bullshit management" and argues that practices such as gathering requirements, maintaining extensive backlogs, and producing management reports have persisted while AI has automated or amplified parts of them. The supplied text presents this as an opinion-based critique, emphasizing that polished outputs may conceal limited customer understanding and learning. ### Source excerpt Four years ago I named it. A million reads later, it's back with better tools. ## ISO 42001 Evidence: What Auditors Ask For DevFeed: [ISO 42001 Evidence: What Auditors Ask For]() Original publisher: [Read original article]() Author: info@goteleport.com (Preet Dhatt) Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [audit](), [AI Development](), [Deployment](), [CI/CD](), [data-governance](), [Monitoring](), [Requirements]() Tags: [ai](), [ai-development](), [article](), [audit](), [ci-cd](), [data-governance](), [deployment](), [eu](), [iso](), [monitoring](), [requirements]() ### AI overview This article explains what auditors look for in ISO 42001 evidence and how organizations can connect access, approvals, infrastructure, CI/CD activity, releases, and production changes into an evidence trail. It also discusses monitoring, human oversight, third-party access, and data governance within an AI Management System. ### Source excerpt Prepare ISO 42001 audit evidence that proves your controls work in practice. ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## Content Governance: Keeping Documentation Trustworthy DevFeed: [Content Governance: Keeping Documentation Trustworthy]() Original publisher: [Read original article]() Author: Selvaraaju Murugesan Published: 2026-08-14T13:14:46Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [Documentation](), [Security](), [audit](), [trust](), [AI Agent](), [Requirements]() Tags: [agent](), [ai](), [audit](), [auditability](), [content](), [documentation](), [governance](), [security](), [technical-writing](), [trust]() ### AI overview This article explains content governance as a structured practice for keeping documentation accurate, secure, compliant, and trustworthy for both human readers and AI agents. It highlights regular audits, quality control, access controls, privacy and compliance checks, and synchronization with software releases. ### Source excerpt Only 21% of business and IT leaders report having a mature governance model ... The post Content Governance: Keeping Documentation Trustworthy appeared first on Document360. ## Navigating SAMA, ADGM & DFSA Requirements with Teleport DevFeed: [Navigating SAMA, ADGM & DFSA Requirements with Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Mukund Cadambi) Published: 2026-08-12T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [Requirements](), [Cybersecurity](), [Cloud](), [audit trail](), [data-governance](), [Architecture & Design]() Tags: [audit-trail](), [cloud](), [compliance](), [cybersecurity](), [data-governance](), [requirements](), [security]() ### AI overview This article explains how SAMA, ADGM, and DFSA requirements affect cloud service providers operating in Saudi Arabia and the UAE. It describes challenges involving data sovereignty, cybersecurity, identity and access controls, privileged and third-party access, and audit evidence, and presents a unified identity layer as an approach to operationalizing compliance. ### Source excerpt Learn how Teleport helps CSPs meet SAMA, ADGM, and DFSA compliance. ## Engineering Practices for Building and Operating AI Systems DevFeed: [Engineering Practices for Building and Operating AI Systems]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Agentic development](), [Testing](), [Spec Driven Development](), [context](), [Requirements]() Tags: [agentic-development](), [requirements](), [spec-driven-development](), [testing]() ### AI overview Harness argues that AI-assisted development needs stronger specifications, testing, operational controls, and failure-mode analysis to maintain delivery quality and manage risk. ### Source excerpt Learn agentic development best practices with spec-driven development, AI system testing, and operational readiness for secure AI delivery. | Blog ## The foundation: Why Temporal for a data pipeline? DevFeed: [The foundation: Why Temporal for a data pipeline?]() Original publisher: [Read original article]() Author: Houman Kargaran Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [data](), [Orchestration](), [pii](), [Requirements](), [Resilience](), [consistency](), [Availability](), [observability](), [AI search](), [Embeddings]() Tags: [auditability](), [community](), [compliance](), [data](), [data-pipeline](), [embeddings](), [execution](), [foundation](), [requirements](), [resilience](), [search](), [temporal]() ### AI overview This tutorial explains how to justify Temporal as the orchestration layer for a PII-compliant complaint-ingestion pipeline. It covers requirements for resilience, consistency, availability, observability, redaction, internal classification, and semantic search over complaint data. ### Source excerpt How to justify Temporal for a regulated data pipeline: the requirements, the naive approach, and why Durable Execution wins on resilience and auditability. ## Why the traditional product manager role is changing DevFeed: [Why the traditional product manager role is changing]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-07-16T12:29:08Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Requirements](), [Artificial Intelligence](), [User Stories]() Tags: [ai](), [requirements](), [software]() ### AI overview The article argues that the traditional product manager role is becoming less relevant as teams adopt Product Engineers and AI-augmented workflows. It says PMs should focus less on exhaustive backlog management and detailed requirements, and more on strategic decisions and critical thinking. ### Source excerpt Most of the role is dying. The part that survives is the part nobody is teaching. ## Essential Non Functional Requirements DevFeed: [Essential Non Functional Requirements]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-07T08:33:34Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [Requirements](), [Architecture & Design](), [Scalability](), [Availability](), [Latency](), [reliability](), [Replication](), [Users]() Tags: [availability](), [data-replication](), [failover](), [haproxy](), [kubernetes](), [latency](), [nginx](), [requirements]() ### AI overview This tutorial explains non-functional requirements as quality attributes that determine how software systems behave under real-world conditions. It covers availability, latency, scalability, and durability, with implementation approaches including load balancers, health checks, failover, CDNs, data replication, horizontal scaling, Kubernetes, and concurrent API design. ### Source excerpt Don't miss them ## You Don't Need to Build It to Design It DevFeed: [You Don't Need to Build It to Design It]() Original publisher: [Read original article]() Author: Humayun Tanwar Published: 2026-07-06T11:05:34Z Content type: tutorial Language: en Sources: [Mindorks - Medium]() Topics: [systems](), [data-architecture](), [Latency](), [Requirements]() Tags: [business](), [code](), [compliance](), [data-architecture](), [design](), [latency](), [machine-learning](), [patterns](), [requirements](), [software-engineering](), [systems](), [tech-stack](), [technology]() ### AI overview This article explains why solutions engineers can contribute meaningfully to system design without writing the implementation. It connects integration scoping, data ownership, real-time communication choices, and failure handling to core design concepts, then presents five questions about scale, constraints, dependencies, failure tolerance, and success criteria. ### Source excerpt System design is one of those topics that feels like it belongs exclusively to engineers who write the code. The diagrams, the architecture reviews, the database choices , it all looks like backend territory from the outside. You're Already Doing This Solutions engineers sit at an interesting intersection. You're deep enough in the technical conversation to understand how systems connect, and close enough to the customer to understand what they actually need from those systems. That combination gives you a view that's genuinely useful in design conversations, and one that's worth developing deliberately. When you scope an integration and map which system owns which data, that's data architecture. When you recommend a webhook approach over polling because the customer needs real-time updates, that's a latency and reliability trade-off. When you ask a customer how they handle failures before a build starts, that's systems thinking. The formal vocabulary of system design is mostly a structured way of describing what you're already doing in the field. Building that vocabulary, and understanding the patterns underneath it, makes it easier to contribute in technical conversations, ask the right questions earlier, and connect your field experience to the broader design decisions being made around you. The Five Questions That Structure Any Design Good design conversations don't start with a diagram. They start with these. Scale. What volume are you designing for, not today but at the ceiling that changes the architecture? A thousand requests a day is a different system from a thousand per second. That number changes your database choice, whether you need a queue, and how retries need to behave. Constraints. What's already fixed? Existing tech stack, compliance requirements, vendor contracts. Every real system gets built inside constraints. Name them before drawing anything. Dependencies. What does this system talk to, and what talks to it? Systems that look simple in a conv ## How to Meet EU Cyber Resilience Act (CRA) Requirements DevFeed: [How to Meet EU Cyber Resilience Act (CRA) Requirements]() Original publisher: [Read original article]() Author: info@goteleport.com (Maximilian Heck, Waldemar Kindler) Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [cyber resilience act](), [Requirements](), [Security](), [audit trail](), [Encryption]() Tags: [audit-trail](), [ci-cd](), [compliance](), [cyber-resilience-act](), [cybersecurity](), [encryption](), [eu](), [how-to](), [requirements]() ### AI overview This tutorial explains how identity, access, encryption, and audit controls relate to EU Cyber Resilience Act requirements. It also discusses fragmented security toolchains, a compromised Trivy release, and infrastructure patterns such as short-lived cryptographic identities, hardware-rooted device trust, and unified audit trails. ### Source excerpt Learn how to implement identity, access, and audit controls that meet EU Cyber Resilience Act compliance requirements. ## LLMs changed writing. But our submission processes haven't caught up. DevFeed: [LLMs changed writing. But our submission processes haven't caught up.]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-05-28T12:55:38Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [LLMs](), [Artificial Intelligence](), [Requirements]() Tags: [ai](), [artificial-intelligence](), [communication](), [evaluation](), [llms](), [optimize](), [submission](), [thoughts](), [writing]() ### AI overview The article argues that LLMs have changed how people create, communicate, and evaluate written information, while many submission and proposal processes still assume that long documents reflect substantial human effort. It recommends prioritizing clarity, concise summaries, key decisions, tradeoffs, and actionable information over document length. ### Source excerpt LLMs are changing how we write, communicate, and evaluate information, but many submission and proposal processes are still designed for a pre-AI era. If AI generates documents and is later summarized by AI, maybe it's time to optimize for clarity, bullet points, and essence instead of length. ## Stop Writing RFCs Like Mystery Novels: Why the Best Design Docs Start with the Conclusion DevFeed: [Stop Writing RFCs Like Mystery Novels: Why the Best Design Docs Start with the Conclusion]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-05-17T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Documentation](), [Requirements](), [Latency](), [Network](), [Back end](), [structure]() Tags: [architecture](), [backend](), [communication](), [conclusion](), [context](), [database](), [docs](), [documentation](), [how-to](), [latency](), [network](), [rfc](), [systems-design](), [writing]() ### AI overview The article argues that RFCs and architecture documents should begin with the conclusion and proposed architecture. It says burying key design decisions in background material overloads reviewers' working memory, encourages superficial approval, and can hide important network and latency risks. ### Source excerpt Reviewers pipeline like CPUs. Hide the proposed architecture on page seven and they load context with no index to file it under, saturate working memory, then skim and rubber-stamp. Invert the structure: conclusion, architecture diagram, hardware and latency impact, then background and rejected alternatives last, so reviewers spend their attention interrogating your network boundaries instead of solving a literary puzzle. ## 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. ## Early and late-stage hypergrowth. DevFeed: [Early and late-stage hypergrowth.]() Original publisher: [Read original article]() Published: 2026-04-27T13:00:00Z Content type: opinion Language: en Sources: [Will Larson - Irrational Exuberance]() Topics: [Scalability](), [Requirements](), [Users]() Tags: [requirements](), [scalability](), [support](), [users]() ### AI overview The article distinguishes early-stage from late-stage hypergrowth. Early-stage companies can focus serially on their biggest specific problems, while late-stage companies must address many simultaneous requirements from a more skeptical market. It argues that adding a new leader can be preferable to expanding an existing leader's scope during late-stage hypergrowth. ### Source excerpt Last week, a colleague asked why I'd hired an additional new leader onto an important area rather than expanding an existing leader's scope to incorporate that area as well. The existing leader was a known quantity and doing well, so why not keep expanding them? It's a good question, and depending on the circumstances I might have done either, but explaining why I specifically brought in a new leader this time depends a bit on a distinction I think of as early versus late-stage hypergrowth. In Cross the Chasm's world, early-stage is when you've proven product market fit, have won the early adopters, and are just starting to win the early majority. In this phase, there are specific problems, and the most important problem is to solve those specific problems. For example, you might be having scalability issues, and solving that is the company's almost sole focus for a few weeks. After scalability is fixed, next you'll need to work on onboarding flows to convert for less technical users, and so on. Not only the executives, but much of the company, serially hunts down solutions to their biggest problem. When you reach late-stage hypergrowth, you are starting to encounter the late majority and laggards cohorts. This reorients the company and executive teams away from only creating an exceptional product, to also having to solve the numerous concerns and checkboxes that a skeptical audience introduces. Sure, your product might save hours a day for our team, but how does your compliance paperwork look? How stable are you? What contractual commitment will you make regarding customer support resolution? At this point, you'll still be in an extremely competitive environment to retain the innovators and early majority, while also having to solve the long list of skeptic-driven requirements. Instead of hunting down solutions, the company-and the executive team-now has to solve everything, everywhere, all at once. Going back to my colleague's question, in early-stage hypergrowth ## Guide: DORA Compliance Evidence for Agentic AI DevFeed: [Guide: DORA Compliance Evidence for Agentic AI]() Original publisher: [Read original article]() Author: info@goteleport.com (Kayne McGladrey) Published: 2026-04-27T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [audit](), [Monitoring](), [Requirements](), [Resilience]() Tags: [agentic-ai](), [audit](), [compliance](), [guide](), [monitoring]() ### AI overview This guide explains how assessors evaluate DORA compliance evidence for agentic AI systems. It distinguishes design effectiveness from operating effectiveness, describes evidence gaps, and emphasizes continuous monitoring and integrity-protected audit trails. ### Source excerpt Learn how to provide audit-ready DORA compliance evidence for agentic AI systems. ## EU AI Act Compliance: Requirements, Risks, and What to Document DevFeed: [EU AI Act Compliance: Requirements, Risks, and What to Document]() Original publisher: [Read original article]() Author: info@goteleport.com (Kayne McGladrey, CISSP) Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Artificial Intelligence](), [Requirements](), [Documentation](), [audit](), [data-governance](), [Logging](), [Monitoring](), [Deployment](), [data]() Tags: [ai](), [compliance](), [cybersecurity](), [data](), [data-governance](), [dataset](), [deployment](), [development](), [documentation](), [eu](), [iso-27001](), [lifecycle](), [logging](), [monitoring](), [nist](), [real-time](), [reproducibility](), [soc-2](), [versioning]() ### AI overview A practical guide to EU AI Act compliance covering requirements, risks, technical documentation, logging, data governance, lifecycle evidence, traceability, monitoring, and human oversight. It outlines key compliance milestones from August 2025 through August 2027. ### Source excerpt Cut through EU AI Act complexity with practical guidance on requirements, risks, and documentation. ## Reliability Engineering for Air-Gapped Systems DevFeed: [Reliability Engineering for Air-Gapped Systems]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-04-03T21:03:06Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [observability](), [systems](), [Resilience](), [on-prem](), [Security](), [Requirements]() Tags: [observability](), [on-prem](), [reliability-engineering](), [resilience](), [security](), [supply-chain-attacks](), [systems](), [work]() ### AI overview This article discusses reliability engineering for air-gapped systems where software teams cannot access runtime data, metrics, or logs. It describes using SLI and SLO practices, alerting, secure operational processes, and on-premises maintenance to manage reliability in systems isolated from the internet. ### Source excerpt Tips and tricks to work around inaccessible observability ## Agentic code workflows with Nick Tune DevFeed: [Agentic code workflows with Nick Tune]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-03-26T16:01:37Z Content type: article Language: en Sources: [Tech World With Milan Newsletter]() Topics: [agentic-coding](), [ai-coding](), [Claude Code](), [Code quality](), [code reviews](), [Test-driven development](), [Testing](), [Command-line interface](), [Domain-driven design (DDD)](), [migration](), [Requirements](), [Continuous Delivery (CD)]() Tags: [agentic](), [agentic-coding](), [agents](), [ai-coding](), [ai-tools](), [best-practices](), [claude-code](), [cli](), [code-quality](), [code-reviews](), [continuous-delivery](), [domain-driven-design](), [migration](), [requirements]() ### AI overview An interview with Nick Tune about advanced agentic coding workflows. It covers planning requirements, modeling development as a tested state machine, implementing features with Claude Code and guardrails, enforcing architecture rules deterministically, reviewing pull requests, applying TDD, and using custom tooling. ### Source excerpt Most engineers who talk about AI productivity talk in abstractions. [Next page]()