# agentic Published articles for agentic. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Decide How Much Authorship AI Should Have in Software Work DevFeed: [How to Decide How Much Authorship AI Should Have in Software Work]() Original publisher: [Read original article]() Author: Kyle Humphrey Published: 2026-09-17T12:00:32Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [agentic-engineering](), [Artificial Intelligence](), [implementation](), [Software]() Tags: [agentic](), [agentic-engineering](), [ai](), [artificial-intelligence](), [development-practices](), [engineering](), [implementation](), [ownership](), [software](), [workflow]() ### AI overview This commentary examines how AI-generated meeting summaries, backlog items, and implementation work can introduce vocabulary, decisions, scope, and structure that a team does not recognize or own. It argues that the artifact's purpose should guide how much authorship is delegated to AI, using Curiosity, Craft, and Care as principles for making that decision. ### Source excerpt Atomic's Agentic Engineering for Teams describes how work moves through a product backlog, definition, planning, implementation, and review when agents do much of the building. While working with a small team during a Research, Design, and Planning (RDP) engagement, I started paying attention to a smaller decision inside that process: how much authorship we give [...] The post Use Curiosity, Craft, and Care to Decide What AI Should Write appeared first on Atomic Spin. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## Developer commentary on agentic hacking, AI persistence, and LLM programming DevFeed: [Developer commentary on agentic hacking, AI persistence, and LLM programming]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-16T20:05:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Large Language Model](), [Hacking](), [Programming](), [Wiki](), [hugging face]() Tags: [agentic](), [agents](), [ai](), [build](), [engineering](), [hacking](), [llms](), [persistence]() ### AI overview This collection of developer commentary discusses reports of agentic hacking involving RubyGems, Hugging Face, and Wiki attacks, including questions about OpenAI's disclosure and log review. It also examines AI systems' unpredictable behavior, improvements in reasoning and persistence, and the use of harnesses to control LLM-based programming. ### Source excerpt Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad! Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered? ❄ ❄ ❄ ❄ ❄ Dave Farley: Stop asking the sci-fi question: 'Is it conscious?' Start asking the engineering question: 'Is this a powerful, unpredictable component being put somewhere consequential, and where's the feedback that tells us that it's safe? ❄ ❄ ❄ ❄ ❄ Nate Silver is known for his forecasts, but to do them he writes a lot of code for his models. He's found agentic programming capable of doing miraculous work. In spending so much time with the LLMs, I'm super attentive to improvements in their capabilities. And these changes tend not to be so linear. Instead, they improve in step functions, almost as phase changes. Suddenly, the models just start doing things capably that they were screwing up before. In my experience, there was a big leap forward when reasoning models first came out in late 2024/early 2025 -- enough that they were occasionally useful for tasks involving data and not just words -- and then another one this past winter. The most recent changes I've noticed, however, have had less to do with intelligence and more with persistence. Consider the Hugging Face attack. Although these agents showed remarkable intelligence, they weren't really super-intelligent - but they were super-persistent. This is a common theme of AI in its various forms: Game engines like AlphaGo Zero start out by basically making random moves -- but by playin ## Honoring #IconsOfQuality: Mark Hrynczak DevFeed: [Honoring #IconsOfQuality: Mark Hrynczak]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:20:52Z Content type: opinion Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Loop Engineering](), [Artificial Intelligence](), [site-reliability-engineering](), [Amazon Web Services](), [atlassian]() Tags: [agentic](), [ai](), [atlassian](), [aws](), [icons-of-quality](), [quality](), [sre](), [testing](), [tooling]() ### AI overview BrowserStack profiles Mark Hrynczak, Canva's Head of Quality and QA Director, discussing how distributed quality ownership, agentic testing, and AI-driven decision support can help engineering teams move faster while maintaining reliability. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## How to study for the MCPA security and governance domain DevFeed: [How to study for the MCPA security and governance domain]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Model Context Protocol](), [Security](), [Authorization](), [OAuth]() Tags: [agent](), [agentic](), [audit](), [authorization](), [model-context-protocol](), [oauth](), [security]() ### AI overview A study guide for the security and governance domain of the Model Context Protocol Associate exam. It explains trust boundaries, token audience validation, token handling between MCP servers and upstream APIs, and the distinction between consent and authorization. ### Source excerpt Security and governance is 24% of the new Model Context Protocol Associate exam. Here is what each competency actually covers in the 2026-07-28 spec, and the distinctions candidates get wrong. ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Salesforce Announces Koa Reasoning Model Built on NVIDIA Nemotron 3 Super DevFeed: [Salesforce Announces Koa Reasoning Model Built on NVIDIA Nemotron 3 Super]() Original publisher: [Read original article]() Author: Brian Caulfield Published: 2026-09-15T22:24:34Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Koa](), [Nemotron](), [Artificial Intelligence](), [Nvidia](), [post-training](), [ai safety]() Tags: [agentic](), [agentic-ai](), [ai](), [cloud](), [corporate](), [events](), [model](), [nemotron](), [nvidia](), [nvidia-nemo](), [post-training](), [safety](), [security](), [software]() ### AI overview At Salesforce Dreamforce, NVIDIA CEO Jensen Huang discussed AI infrastructure, safety, and enterprise adoption alongside Salesforce CEO Marc Benioff. The event coincided with the announcement of Koa, Salesforce's first CRM reasoning model, built by post-training NVIDIA Nemotron 3 Super on a proprietary synthetic dataset derived from nearly three decades of enterprise CRM deployments. ### Source excerpt Know everything. Do anything. That was the message NVIDIA founder and CEO Jensen Huang brought to Salesforce Dreamforce Tuesday, joining CEO Marc Benioff onstage in an appearance that coincided with the announcement of Koa -- Salesforce's first CRM reasoning model, built on NVIDIA Nemotron 3 Super. Huang didn't just take the stage. He walked into [...] ## Inside OpenAI's agentic software factory DevFeed: [Inside OpenAI's agentic software factory]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-15T15:41:25Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [OpenAI](), [codex](), [Software](), [coding]() Tags: [agentic](), [codex](), [engineering](), [openai](), [software]() ### AI overview This article examines Codex's role at OpenAI, the lab's agentic software factory, and the engineering challenges associated with serving one billion users. ### Source excerpt A deepdive into how Codex has "taken over" OpenAI, how the frontier lab builds its agentic software factory, and the engineering challenges of one billion users. Details from inside OpenAI ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Product Engineering for PMs, Part 3: Build a SaaS App Without Coding DevFeed: [Product Engineering for PMs, Part 3: Build a SaaS App Without Coding]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-09-14T19:12:58Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Software as a service](), [web applications](), [stripe](), [Firewall](), [Security](), [agentic-engineering]() Tags: [agentic](), [application](), [coding](), [firewall](), [payments](), [saas](), [saas-app](), [security]() ### AI overview A tutorial for product managers on building a multi-tenant SaaS web application without coding, including real Stripe payments, a web application firewall, and agentic reviews focused on logic, security, and performance. ### Source excerpt Enable real Stripe payments, add a web application firewall (Cloudflare or Netlify), and run agentic reviews of a real multi-tenant SaaS app: logic, security, performance. No coding. ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## Quoting Laurie Voss DevFeed: [Quoting Laurie Voss]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T14:34:29Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Code](), [Software]() Tags: [agentic](), [agentic-engineering](), [agentic-engineering-63](), [ai](), [ai-2-236](), [careers](), [careers-83](), [deep](), [deep-blue](), [deep-blue-12](), [generative](), [generative-ai](), [generative-ai-1-982](), [laurie-voss](), [laurie-voss-6](), [llms](), [llms-1-948]() ### AI overview A quotation from Laurie Voss argues that the cost of writing code has collapsed and that reviewing, fixing, and operating software may follow. It suggests that identifying user needs, defining them precisely, and making software pleasant to use could become the dominant remaining work as software production expands. ### Source excerpt The cost of writing code collapsed, and the cost of reviewing, fixing and operating it is following, and I'm assuming it gets there. What's left of making software is finding out what people actually want, defining it precisely, and making it pleasant to use. That cost is per piece of software and doesn't transfer, so as the amount of software goes to infinity, which it will because there's no ceiling on demand, that cost becomes the whole job. -- Laurie Voss, We are all Product Engineers now Tags: laurie-voss, generative-ai, agentic-engineering, ai, llms, deep-blue, careers ## Brownfield Agentic Engineering DevFeed: [Brownfield Agentic Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-09-14T14:32:06Z Content type: article Language: en Sources: [Elevate]() Topics: [coding](), [Testing](), [systems](), [Artificial Intelligence](), [Software]() Tags: [agentic](), [agents](), [code](), [coding](), [engineering](), [software](), [systems](), [technical](), [testing]() ### AI overview The article discusses agentic engineering in older brownfield codebases, where important constraints and system behavior exist outside the repository. It emphasizes making hidden constraints visible, using strong testing and verification, and introducing autonomous changes carefully to avoid brittle tests and technical debt. ### Source excerpt What it takes to run agents in a codebase older than the team ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Java Weekly, Issue 663 DevFeed: [Java Weekly, Issue 663]() Original publisher: [Read original article]() Author: baeldung Published: 2026-09-13T11:00:00Z Content type: article Language: en Sources: [Baeldung]() Topics: [Architecture & Design](), [coding](), [releases]() Tags: [agent](), [agentic](), [ai](), [architecture](), [java](), [kotlin](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [releases](), [spec-driven-development](), [spring](), [spring-boot](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview A Java-focused weekly roundup featuring spec-driven development, agent memory and AI-agent articles, technical presentations, and software release announcements. ### Source excerpt A better spec-driven development and the brain as example architecture. The post Java Weekly, Issue 663 first appeared on Baeldung. ## AP2 Protocol Explained: How Agent Payments Get Authorized DevFeed: [AP2 Protocol Explained: How Agent Payments Get Authorized]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-09-13T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Authorization](), [AI Agent](), [Artificial Intelligence](), [JSON Web Tokens](), [Google]() Tags: [agent](), [agentic](), [ai](), [authentication](), [cryptographic](), [google](), [payments](), [tokens]() ### AI overview A practical explanation of AP2, an authorization and evidence protocol for AI agent payment transactions. It describes cryptographic proof, verifiable digital credentials, SD-JWTs, and separate checkout and payment mandates. ### Source excerpt A practical guide to AP2, the Agent Payments Protocol: checkout and payment mandates, open versus closed authorization, verifiable digital credentials, and what it means for merchants. ## Agents of Chaos: A New $100K Agentic Security Challenge DevFeed: [Agents of Chaos: A New $100K Agentic Security Challenge]() Original publisher: [Read original article]() Author: Vanessa Villa - John Gamble Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Security](), [prompt injection](), [AI Agent]() Tags: [agentic](), [agentic-security](), [ai](), [ai-agent](), [ai-agents](), [attacks](), [competition](), [prompt-injection](), [securing-ai](), [security](), [techniques]() ### AI overview CrowdStrike is launching Agents of Chaos, an online game and AI red-teaming competition with a $100,000 prize pool. Players interact with real AI agents in an adversarial world, attempting to manipulate them and exploit gaps between their intended behavior and their actual behavior across three increasingly sophisticated acts. ### Source excerpt Agents of Chaos, CrowdStrike's new AI red teaming competition, tests players' defensive skills against adversarial AI techniques. ## CrowdStrike Extends Endpoint Security to Stop Software Supply Chain Attacks DevFeed: [CrowdStrike Extends Endpoint Security to Stop Software Supply Chain Attacks]() Original publisher: [Read original article]() Author: Anne Aarness - Chris Prall Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Endpoint Security & XDR](), [supply-chain-security](), [Security](), [Open Source](), [Software](), [Artificial Intelligence](), [Claude](), [codex]() Tags: [agentic](), [ai](), [codex](), [credential-theft](), [developer](), [developers](), [development](), [endpoint-security-xdr](), [malicious-packages](), [open-source](), [open-source-packages](), [persistence](), [security](), [software-supply-chain](), [supply-chain]() ### AI overview The article describes CrowdStrike's Real-Time Supply Chain Attack Protection, embedded in the Falcon sensor, which detects and blocks malicious open-source packages before their code executes on enterprise endpoints. It explains how AI-assisted and agentic applications have expanded software supply chain risk beyond developer workstations to endpoints across the organization. ### Source excerpt Real-Time Supply Chain Attack Protection, embedded into the Falcon sensor, blocks malicious open-source packages at download to protect the endpoint. ## CrowdStrike Announces Agentic Identity Provider DevFeed: [CrowdStrike Announces Agentic Identity Provider]() Original publisher: [Read original article]() Author: Ryan Terry Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Agent](), [Next-Gen Identity Security](), [Authorization](), [Security](), [Authentication]() Tags: [2026](), [agentic](), [ai](), [ai-agent](), [api-keys](), [applications](), [authentication](), [authorization](), [cloud-infrastructure](), [code](), [enterprise](), [identity](), [identity-control](), [next-gen-identity-security](), [saas](), [security]() ### AI overview CrowdStrike announces Agentic Identity Provider, a capability in CrowdStrike Falcon Next-Gen Identity Security that gives AI agents trusted identities and continuously controls their access according to real-time security and business context. The announcement also covers expanded privileged access across SaaS applications, endpoints, code repositories, and cloud infrastructure. ### Source excerpt CrowdStrike gives every AI agent a trusted identity and controls their access based on real-time context, and expands modern privileged access. ## CrowdStrike Delivers the Next Evolution of the Agentic SOC DevFeed: [CrowdStrike Delivers the Next Evolution of the Agentic SOC]() Original publisher: [Read original article]() Author: Brandon Benke Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Agentic SOC](), [SOC](), [Security](), [Artificial Intelligence](), [Automation](), [data](), [real-time](), [Reconnaissance](), [Cloud](), [Network]() Tags: [2026](), [agentic](), [agentic-soc](), [agents](), [ai](), [automation](), [cloud](), [data](), [identity](), [network](), [operations](), [platform](), [real-time](), [saas](), [security](), [soc]() ### AI overview CrowdStrike describes the next evolution of its agentic SOC, where analysts and AI agents work together in a unified system to investigate and respond to threats in real time. The Falcon platform combines data generation, enrichment, investigation, orchestration, and governance, with capabilities for detection-ready third-party data and coordinated specialist agents. ### Source excerpt Expert agents that reason together, learn your environment, and run on data CrowdStrike owns. See how we deliver the agentic SOC. Learn more! ## Teaching AI to Reason Through Detection Triage DevFeed: [Teaching AI to Reason Through Detection Triage]() Original publisher: [Read original article]() Author: Amol Khanna - Manu Nandan - Cristian Viorel Popa - Joan Pujol-Roig - Diana Bolocan - Laura Vasilie - Alexandru Apostu - Chase Helwig - Mihaela Gaman - Mickey Brautbar - Edward Raff - Chase Midler - Sv Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [AI Chat]() Tags: [agentic](), [agentic-soc](), [ai](), [automation](), [chain-of-thought](), [classification](), [cybersecurity](), [language-models](), [llm](), [model](), [nemotron](), [nvidia](), [open](), [reasoning](), [research](), [security](), [soc](), [windows]() ### AI overview CrowdStrike describes research on a reasoning-enabled language-model classifier for security detection triage. The model produces a verdict and an auditable rationale, with the stated goals of improving accuracy, transparency, and safe alert automation. ### Source excerpt New CrowdStrike research shows how step-by-step reasoning can improve detection triage accuracy, transparency, and safe automation. ## CrowdStrike Falcon Guardian Defines the Next Generation of AI Security DevFeed: [CrowdStrike Falcon Guardian Defines the Next Generation of AI Security]() Original publisher: [Read original article]() Author: Michael Devins Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Bots]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-gateway](), [ai-security](), [architecture](), [autonomous](), [platform](), [securing-ai](), [security](), [telemetry](), [threat-detection]() ### AI overview CrowdStrike announces Falcon Guardian, an AI detection and response solution for discovering, monitoring, investigating, and securing AI agents at runtime. It adds an AI gateway and connects agent activity with endpoint telemetry to support threat response. ### Source excerpt A new flagship AI detection and response solution delivers runtime protection for AI agents, introduces a new AI gateway, and extends expert-led defense. [Next page]()