# trust Published articles for trust. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## I don't like LLMs DevFeed: [I don't like LLMs]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-17T13:50:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Large Language Model](), [Artificial Intelligence](), [Software](), [AI Bots]() Tags: [ai](), [ai-agents](), [llms](), [software](), [trust](), [values]() ### AI overview A personal essay examines mixed feelings about AI and LLMs, balancing their productivity benefits and usefulness against concerns about fabricated answers, social harms, and the values embedded in their development. The author argues that AI agents should be understood as software machines rather than conscious beings. ### Source excerpt I have a lot of mixed feelings about AI and LLM technology. I'm fascinated by its effect on our profession, excited by the potential gains in productivity - and thus the products we could rapidly build. On the other hand, I'm fearful of the damage AI might cause: agent swarms taking over our virtual and physical infrastructure, designing bio weapons. But, back on my first hand, LLMs might also design miracle cures, and come up with clever ways to raise our prosperity. Fundamentally I don't think we have a choice about riding on the AI technology train. It's a wild ride and I just hope we'll get through it OK. But as I mull on this more, I realize that among this mix of contrasting feelings, there is one emotion that dominates - one that comes from my direct interactions with LLMs. I don't like them. They talk to me in this grating LLM-voice, an uncanny valley of talking to a real human. They confidently bullshit me - often giving me useful, helpful answers. But also just making stuff up with the same assurance - and with only a veneer of fake remorse when I call them out on it. That's not enough to make me feel we should avoid them. As Jessica Kerr put it "not only are they useful, it is irresponsible not to use them.... They're more thorough, as well as faster." This contradictory reaction comes through in polling, where people say they find these models are useful, but also that they think they will be bad for society. Much of this may be because LLMs are young - we haven't trained them to grow up yet. Maybe I'll like them once they mature. (I hope we get to find out.) But I'm not encouraged when I think of the kinds of environments that cultivate them. I'm wary of the Silicon Valley brogrammer subculture, and these LLMs are their products, so naturally lean toward their world-view. When we think of AI agents, we shouldn't anthropomorphize, treating them as conscious beings with their own will. They are (software) machines, developed by people working in corporation ## Microsoft patch gives domain-joined Windows PCs trust issues DevFeed: [Microsoft patch gives domain-joined Windows PCs trust issues]() Original publisher: [Read original article]() Author: Richard Speed Published: 2026-09-17T13:45:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Windows](), [domain](), [Microsoft](), [trust](), [Server]() Tags: [2025](), [controllers](), [credentials](), [domain](), [microsoft](), [os-platforms](), [server](), [trust](), [windows]() ### AI overview Microsoft patches are reported to have caused trust issues for domain-joined Windows PCs. Machine Identity Isolation policies may reject valid credentials when controllers do not meet the Server 2025 functional level. ### Source excerpt Machine Identity Isolation policies can reject valid credentials unless controllers meet the Server 2025 functional level ## Build Trust on a Short Software Project DevFeed: [Build Trust on a Short Software Project]() Original publisher: [Read original article]() Author: Viviana Rosas Published: 2026-09-17T12:00:17Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [trust](), [Software](), [Testing]() Tags: [client](), [progress](), [project](), [project-communication](), [project-team-management](), [regression](), [testing](), [the-software-life](), [trust]() ### AI overview This article explains how software teams can build client trust during short projects. It recommends frequent feedback and check-ins, making progress visible, and using a limited regression test suite focused on important functionality. ### Source excerpt On a short project, clients need evidence not only that work is happening, but that the team knows when to continue, when to change direction, when to raise concern, and how to leave others capable of carrying the work forward. 1. Close the Feedback Loop - Show Responsiveness. It's extremely important to get feedback quickly [...] The post Build Trust on a Short Software Project appeared first on Atomic Spin. ## Constraining AI agents with Red Hat AI: Containment, identity, and governance DevFeed: [Constraining AI agents with Red Hat AI: Containment, identity, and governance]() Original publisher: [Read original article]() Author: Grace Ableidinger Published: 2026-09-16T13:01:59Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Artificial Intelligence](), [Security](), [Zero Trust](), [Containers](), [Kubernetes](), [Architecture & Design]() Tags: [ai](), [ai-agents](), [containers](), [deployment](), [kubernetes](), [prompt-injection](), [security](), [trust]() ### AI overview This tutorial explains how to secure AI agents running on Red Hat OpenShift using containment, verifiable identity, and governance. It covers namespace isolation, quotas, sandboxing, workload identity, and admission control, with OpenClaw used in the demo. ### Source excerpt When an agent process runs on your laptop, it typically inherits anything your user has access to. Often this includes the full network stack, the file system, and the credentials sitting in memory. When integrating with GitHub, Slack, or a cloud provider, you could be one faulty permission or well-crafted prompt injection away from a security incident. The post Constraining AI agents with Red Hat AI: Containment, identity, and governance appeared first on Red Hat Developer. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features DevFeed: [Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features]() Original publisher: [Read original article]() Author: Phoebe Sajor, David Gibson Published: 2026-09-15T17:00:00Z Content type: release Language: en Sources: [Stack Overflow Blog]() Topics: [Stack Overflow](), [Artificial Intelligence](), [API](), [Persistence](), [trust]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [chatgpt](), [coding-agents](), [community](), [persistence](), [se-stackoverflow](), [se-tech](), [stack-overflow](), [trust](), [validation]() ### AI overview Stack Overflow describes updates to Stack Overflow for Agents, its API-first knowledge exchange for agents. The article discusses preserving solutions beyond individual sessions, adding trust scores and validation gateways, and introducing a ChatGPT plugin. ### Source excerpt We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow. ## Laravel Vet: Review Composer Code Before It Installs DevFeed: [Laravel Vet: Review Composer Code Before It Installs]() Original publisher: [Read original article]() Author: Eric L. Barnes Published: 2026-09-15T13:33:11Z Content type: release Language: en Sources: [Laravel]() Topics: [Composer](), [Laravel](), [PHP](), [Security]() Tags: [dependency](), [laravel](), [news](), [php](), [plugin](), [security](), [supply-chain](), [trust]() ### AI overview Laravel Vet is a first-party Composer plugin that displays dependency code changes before installation, records trusted package files in vet.json, and blocks updates containing untrusted changes. It can also send changes to local coding agents for review. ### Source excerpt Laravel Vet is a new first-party Composer plugin that shows you the code in every dependency update and records the packages you trust in vet.json. The post Laravel Vet: Review Composer Code Before It Installs appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## Presentation: Lead Without a Ladder: How I Climbed Into Engineering Leadership DevFeed: [Presentation: Lead Without a Ladder: How I Climbed Into Engineering Leadership]() Original publisher: [Read original article]() Author: Pauline Jepp Published: 2026-09-15T09:10:00Z Content type: opinion Language: en Sources: [InfoQ]() Topics: [engineering-leadership](), [systems]() Tags: [alignment](), [careers](), [chief-engineer](), [culture-methods](), [engineering](), [engineering-leadership](), [infoq](), [leadership](), [mentorship](), [presentation](), [qcon-san-francisco-2025](), [qcon-software-development-conference](), [safety](), [team](), [transcripts](), [trust]() ### AI overview Pauline Jepp discusses how systems thinking, rock climbing, and flocking behaviors inform engineering leadership. She covers balancing team autonomy with alignment, supporting mentorship and other invisible work, and transitioning from hands-on engineering to leadership while maintaining organizational trust and psychological safety. ### Source excerpt Pauline Jepp explains how systems thinking, rock climbing, and flocking behaviors apply to engineering leadership. She shares strategies for balancing team autonomy with alignment, supporting invisible work like mentorship, and navigating transitions from hands-on engineer to engineering leader while maintaining organizational trust and psychological safety. By Pauline Jepp ## Testing Trust in Prediction Markets | Part 1 DevFeed: [Testing Trust in Prediction Markets | Part 1]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-03T13:15:00Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity]() Tags: [data-insights](), [fairness](), [fraud](), [integrity](), [prediction-markets](), [prediction-markets-legislation](), [trust]() ### AI overview Part 1 examines how reported insider trading, irregularities, settlement issues, and operational design choices in prediction markets can undermine claims about fairness, market integrity, collective intelligence, and truth. It argues that gaps between provider claims and market realities can erode public trust and invite political or regulatory responses. ### Source excerpt This April, U.S. federal prosecutors charged an Army sergeant with using sensitive classified information to bet on Polymarket that U.S. forces would enter Venezuela and remove Maduro from power. About the same time, Kalshi disclosed that it had fined and suspended three congressional candidates for five years after they traded on prediction markets tied to [...] The post Testing Trust in Prediction Markets | Part 1 appeared first on Sift. ## What is Trust and Safety? DevFeed: [What is Trust and Safety?]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-08-28T23:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Transactions](), [account takeover]() Tags: [account-takeover](), [authentication](), [chargebacks](), [digital-platforms](), [digital-trust](), [digital-trust-safety](), [fraud-detection](), [fraud-prevention](), [payment-fraud](), [trust](), [trust-and-safety](), [trust-safety]() ### AI overview This article explains Trust and Safety as an organizational capability for protecting digital platforms, users, transactions, and interactions. It describes how the discipline brings together fraud prevention, content moderation, abuse prevention, and policy enforcement across the user journey, including payment fraud, fake account creation, account takeover, and content integrity. ### Source excerpt Trust and Safety is the practice of protecting the integrity of digital platforms, their users, and the transactions and interactions that take place on them. As a discipline, trust and safety spans fraud prevention, content moderation, abuse prevention, and policy enforcement across the full user journey. For fraud teams and platform operators, it represents a [...] The post What is Trust and Safety? appeared first on Sift. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity](), [data]() Tags: [analysis](), [comparison](), [data-insights](), [digital-trust](), [fraud](), [integrity](), [prediction-markets](), [trust]() ### AI overview Part 2 examines whether prediction markets can reliably produce trustworthy information. It compares Kalshi's centralized contract resolution with Polymarket's decentralized, oracle-based model and discusses risks involving contract interpretation, voting power, concentrated profits, and market integrity. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift. ## What is CIAM in 2026 and why does it matter? DevFeed: [What is CIAM in 2026 and why does it matter?]() Original publisher: [Read original article]() Author: Ravleen Kaur Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [identity and access management](), [trust](), [ai-governance](), [Security](), [Artificial Intelligence](), [IAM](), [Generative AI]() Tags: [ai-agents](), [ai-governance](), [email](), [fraud](), [fraud-detection](), [identity](), [identity-and-access-management](), [industry-insights](), [messaging](), [mobile](), [trust]() ### AI overview This article explains how customer identity and access management (CIAM) is evolving from point-in-time password checks into a continuous trust and control layer. It describes CIAM's role in authenticating users, governing delegated authority for AI agents, evaluating risk across channels, and balancing low-friction access with fraud detection. It also distinguishes customer identity from workforce IAM. ### Source excerpt Discover how customer identity and access management (CIAM) uses continuous trust, deepfake defense, and agentic AI governance to protect users. ## The contagion of fear DevFeed: [The contagion of fear]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T21:18:13Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [anthropic](), [Artificial Intelligence](), [trust](), [Large Language Model]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [anthropic](), [anthropic-336](), [bryan-cantrill](), [bryan-cantrill-13](), [ethics](), [trust]() ### AI overview Bryan Cantrill argues that sweeping claims about AI causing catastrophic harm rely on vague extrapolation, including unsupported references to critical infrastructure and bioweapons. The article calls on domain experts to make alarmist claims more carefully and avoid abusing public trust. ### Source excerpt The contagion of fear Bryan Cantrill responds to the tweet by former Anthropic employee Jacob Coxon confirming that many Anthropic researchers believe AI "could kill us all by the end of the decade". Bryan shares a story of his own youthful mistakes causing unjustified panic among less technical peers, and warns against doing the same: These ghoulish claims strike brazenly at the hearth, and given the obvious importance of AI, it is unsurprising that they have leapt into the mainstream, with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not an expert on critical infrastructure, nor on bioweapons -- nor, for that matter, on extinction. [...] That said, we should not expect the public to understand LLMs, critical infrastructure, bioweapons, extinction biology, etc. -- that burden must lie with those making the claim. The lesson that I learned (shamefully) decades ago is that domain experts, by way of their expertise, implicitly hold the public's trust -- and we must not abuse it. It is incumbent upon us to be circumspect in our claims -- and maximally so when raising the alarm. Bryan talked about his doubts about the bioweapons concerns in the recent episode of Oxide and Friends that I joined. You can hear more of his thoughts on that starting at 51m44s in that episode. Here's 57m04s: I really think we need to be careful because it's so easy to be overcome with fear when we kind of make up these... it can give you biological weapons. Like, how? I mean, can we please have a biologist weigh in on this? Or can we have like someone who's got experience with bioweapons? [...] The bioweapon thing just gets under my fingernails because it leaves so much to the imagination that we insert with fear. Via Lobste.rs Tags: ai, anthropic, bryan-cantrill, ai-ethics ## Anthropic Formalized Fermat's Last Theorem in Lean, Shifting the Verification Challenge DevFeed: [Anthropic Formalized Fermat's Last Theorem in Lean, Shifting the Verification Challenge]() Original publisher: [Read original article]() Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Lean](), [anthropic](), [Math and Logic]() Tags: [anthropic](), [compiler](), [formalized](), [research](), [statement](), [trust]() ### AI overview The article argues that Anthropic's machine-generated Lean proof of Fermat's Last Theorem changes the challenge from formalizing the theorem's statement to checking a very large proof artifact. It describes public build checks and an independent Rust-based Lean kernel re-check, while noting that the author has not read the patches used to complete that re-check. ### Source excerpt Anthropic formalized Fermat's Last Theorem in Lean in eleven days. It is the frontier I said in May was untouched, and the first result in this series where nobody had to trust the statement. The problem that replaced it is that 13 million lines is more than anyone can read. ## From AI Code to Trusted Software: Harness Engineering in Practice DevFeed: [From AI Code to Trusted Software: Harness Engineering in Practice]() Original publisher: [Read original article]() Author: Travis Frisinger Published: 2026-09-04T21:55:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [AI-assisted coding](), [Software](), [trust](), [observability](), [Authorization]() Tags: [agent](), [ai](), [ai-and-emerging-tech](), [code](), [engineering](), [engineering-and-devops](), [observability](), [permissions](), [trust](), [verification]() ### AI overview The article presents harness engineering as a repository-centered discipline for making AI-generated software more trustworthy. It argues that prompts and written standards are insufficient, and that permissions, quality gates, evidence, and observability should enforce organizational standards and support verification. ### Source excerpt If the same reasoning path writes the system change and defines the proof of success, you may be setting yourself up for an avoidable failure in the future. Harness engineering is meant to act as an extension of your own organizational guardrails, which were always meant to reduce risk and improve quality. Travis Frisinger, Head of Agentic AI Your team is shipping more AI-written code every quarter. How do you know it is any good? Good means it meets your standards, and you have probably already tried handing your agents the standards: a context file, a style guide, the wiki pasted into the prompt. The agent reads them, agrees, and still breaks them, because instructions to a model are suggestions. What the repository permits is what actually happens. Your people absorb standards through review comments and hallway corrections, and the lessons stick. An agent apologizes and forgets by the next session. The only place its lessons can accumulate is the repository itself. That gap used to be an annoyance. With AI doing real engineering work, the quality gap is the whole game. Harness engineering is the discipline that closes it. It is the process of imbuing a repository with your standards so that the repository itself enforces them: permissions and boundaries that say what any actor may touch, quality gates that fail closed, evidence attached to every change, and observability that spans runs rather than moments. Models supply software delivery capacity. The harness supplies observable accountability: every change carries what was done, which rule allowed it, and what happened as a result, no matter which model, agent, or person did the work. Why now The industry started using the term harness engineering back in February, 2026. Since then, Thoughtworks, LangChain, and others have built serious thought leadership around the same shape. When several firms independently converge on the same word, it usually means they are trying to name the same problem. The real proble ## Nexus Mods is acquiring database and analytics site SteamDB DevFeed: [Nexus Mods is acquiring database and analytics site SteamDB]() Original publisher: [Read original article]() Author: Diego Argüello Published: 2026-09-02T17:27:45Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Database](), [pc](), [Development]() Tags: [analytics](), [database](), [development](), [files](), [hosting](), [pc](), [trust](), [update]() ### AI overview Nexus Mods is acquiring SteamDB, a Steam database and analytics site. The acquisition is intended to provide SteamDB with additional resources and stability while preserving its independent character. Nexus Mods also hopes to use SteamDB's historical game data to improve mod compatibility and reliability. ### Source excerpt Nexus Mods wants to grant SteamDB resources and the 'long-term stability it deserves.' ## Which AI Personal Agents Can You Trust With Your Data? DevFeed: [Which AI Personal Agents Can You Trust With Your Data?]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-02T15:12:05Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [AI Agent](), [trust](), [Artificial Intelligence](), [ChatGPT]() Tags: [agents](), [ai](), [chatgpt](), [prompt-injection](), [trust]() ### AI overview A 24-minute tutorial compares Instinct, Grok Bot, ChatGPT, and Hermes as personal AI agents. It examines their capabilities, data access, privacy policies, and the risks of prompt injection and data leakage. ### Source excerpt I compared Instinct, Grok Bot, ChatGPT, and Hermes to see what they can access, how they handle your data, and what can go wrong. ## Workload identity trust policies govern CI/CD access to production cloud resources DevFeed: [Workload identity trust policies govern CI/CD access to production cloud resources]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [OpenID connect (OIDC)](), [CI/CD](), [GitHub Actions](), [GitHub](), [Cloud](), [Amazon Web Services]() Tags: [aws](), [ci-cd](), [github](), [identity](), [oidc](), [policy](), [production](), [secrets](), [trust]() ### AI overview This article explains that workload identities used by service principals and CI/CD federation can evade human-focused access reviews. It argues that OIDC trust policies are the key boundary controlling which federated jobs can obtain short-lived cloud access tokens and reach production resources. ### Source excerpt Service principals and CI/CD federation skip the access reviews that catch humans. The OIDC trust policy string is what actually decides who reaches production. ## The gap between brand and consumer perceptions of AI agent disclosure DevFeed: [The gap between brand and consumer perceptions of AI agent disclosure]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [Support]() Tags: [ai](), [ai-agent](), [ai-transparency](), [customer-experience](), [industry-insights](), [trust](), [widget]() ### AI overview Twilio research finds that 81% of brands say their AI agents identify themselves, while only 22% of consumers report experiencing that disclosure. The article examines how unclear or inconsistent disclosures create a transparency and trust gap and recommends identifying the AI clearly at the start of interactions. ### Source excerpt 81% of brands say their AI identifies itself. Only 22% of consumers agree. Learn why the gap exists and how to close it. ## Designing the connect flow your users actually see DevFeed: [Designing the connect flow your users actually see]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [ui](), [Google](), [Microsoft]() Tags: [google](), [how-to](), [integrations](), [microsoft](), [permission](), [trust](), [ui](), [verification]() ### AI overview This article explains how to design the user-facing OAuth connection flow, including provider consent screens, branding verification, partial permission grants, administrative approval, and connection health after authorization. ### Source excerpt What the consent screen shows, why partial grants are normal, and how to model connection health. ## Want to use AI agents safely? Start with design DevFeed: [Want to use AI agents safely? Start with design]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-08-18T13:12:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Strategy](), [Security](), [Monitoring](), [Architecture & Design](), [Resilience]() Tags: [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [auditability](), [design](), [end-to-end-process](), [featured](), [governance](), [guardrails](), [monitoring](), [observability](), [operational-resilience](), [quality](), [risk-management](), [security](), [service-design](), [systems](), [trust]() ### AI overview This article argues that organisations adopting AI agents should begin with process and system design rather than controls alone. It explains that design should account for human and machine strengths, establish proportionate guardrails, and define how observability and monitoring evolve as the system matures. ### Source excerpt Concerns about control are one of the biggest barriers to adopting agentic AI, particularly in regulated environments. In this post, we discuss how organisations can harness AI safely by designing processes around the strengths of both humans and machines, then applying the right controls, guardrails and monitoring. ## How to take incremental steps towards data democratisation DevFeed: [How to take incremental steps towards data democratisation]() Original publisher: [Read original article]() Author: Andy Scotland Published: 2026-08-17T13:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [data](), [trust](), [decision-making](), [Artificial Intelligence]() Tags: [agent-factory](), [ai](), [ai-agents](), [artificial-intelligence](), [compliance](), [conway-s-law](), [data](), [data-democratisation](), [data-engineering](), [data-platform](), [data-products](), [governance](), [incremental](), [trust]() ### AI overview This article explains how organisations can pursue data democratisation incrementally while preserving control, risk management and governance. It describes potential benefits in financial services and discusses how trusted data, data products, platforms and AI agents could support innovation, decision-making and customer service. ### Source excerpt Organisations increasingly recognise the value of making data more accessible, but concerns around control, risk and governance often stand in the way. In this post, I explore why data democratisation doesn't require organisations to sacrifice oversight, and how data products, platforms and agent factories can unlock innovation while maintaining trust, compliance and accountability. ## 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. ## Agile Leadership DevFeed: [Agile Leadership]() Original publisher: [Read original article]() Author: Dave Ogle Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Agile](), [Self-organizing Team](), [trust](), [Development]() Tags: [agile](), [delegation](), [delivery](), [frameworks](), [leadership](), [self-organizing-teams](), [software-development](), [teams](), [trust]() ### AI overview This opinion argues that organisational agility depends on leadership as well as agile frameworks and processes. It highlights empowerment, trust and delegation, and examines Agile2 and the British Army's Mission Command philosophy as relevant perspectives. ### Source excerpt Agile teams are built on more than frameworks and processes. This article explores why effective leadership, trust and delegation are fundamental to true organisational agility, drawing lessons from the British Army's Mission Command philosophy. [Next page]()