Agentic AI

Evidence (20)

• 9Hacker News·10/1/2026breakthrough
  OpenAI and Synopsys announce GPT-Synopsys for chip design

  OpenAI and Synopsys announced a strategic multi-year partnership to develop GPT-Synopsys, a specialized model for semiconductor chip design. The companies said OpenAI will license Synopsys EDA tools for model development and that they will collaborate on R&D and go-to-market efforts under a shared revenue framework.

• 6The New Stack·9/27/2026research
  Agentic AI is reshaping Kubernetes operations

  The article argues that agentic AI is emerging as a new infrastructure layer for Kubernetes management, especially as AI workloads push more deployment, scaling, and governance responsibility onto platform teams. It says effective use depends on giving agents enough cluster state, policy, and access context while strictly limiting what they can observe, recommend, and change.

• 4The New Stack·9/9/2026security
  CISO roundtable to debate AI autonomy in SOCs

  Security leaders are discussing how far AI agents should be allowed to go in security operations centers, from investigating alerts to taking response actions such as account disabling or endpoint isolation. The article highlights the need for clear limits, human oversight, and rollback controls as teams experiment with more autonomous security workflows.

• 7The New Stack·9/8/2026product_launch
  Ex-HashiCorp CEO Dave McJannet launches Dome to govern enterprise AI agents

  Dave McJannet, former HashiCorp CEO, is building Dome Systems to help enterprises govern AI agents that make probabilistic decisions and use multiple tools and backend systems. The company launched from stealth with seed funding and is positioning its platform around agent registry, tool control, and policy enforcement across the agent stack.

• 5InfoQ·9/7/2026research
  From AI Agent Demo to Production: Automated Testing

  Zhou Yu explains why AI agents often fail to move beyond demo environments and argues that simulation-driven testing can address compliance and reliability issues. The presentation highlights how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents and detect edge cases before deployment.

• 2Hacker News·9/7/2026research
  Essay argues programming is an art form

  The post is a personal essay contrasting people who write code for income or product-building with those who code for the craft itself. It argues that AI may speed up development for some, but it will not replace programmers who see coding as a creative process and continue to write by hand.

• 6InfoQ·9/6/2026security
  Figma Uses AI Agents to Speed Security Investigations

  Figma documented how its engineering team built AI agents to assist the security team with alert investigation, incident search, system checks, and draft code fixes. The company says the agents learn from prior investigations and help resolve complex alerts about 70% faster.

• 4InfoQ·9/5/2026research
  Predictions for Software Engineering at QConAI 2030

  Meryem Arik outlines how software engineering may change by 2030, with token spend management, parallel agent infrastructure, and non-technical builders becoming more important. She also highlights agent-driven vendor choices, possible regulatory challenges, and a shift for engineers toward product leadership and multi-agent coordination.

• 4The New Stack·9/3/2026research
  Retrieval engineering to scale AI agents

  The article argues that as AI agents are deployed at larger scale, retrieval infrastructure becomes a bottleneck because many agents can generate concurrent data queries and strain freshness, latency, and relevance. It highlights a live discussion on rebuilding retrieval as a unified layer rather than a fragmented stack of separate systems.

• 5The New Stack·9/2/2026framework_update
  AI fluency, not just access, needed for adoption

  The article argues that organizations prioritizing AI adoption often fail when they focus on broad tool access instead of building AI fluency across teams. It recommends a hub-and-spoke operating model, with a central governance team and AI engineers embedded in departments to identify use cases, rework processes, and share reusable patterns.

• 4OpenAI Blog·9/1/2026framework_update
  How AI-native companies turn workflows into operating capability

  OpenAI highlights how Basis, Clay, and Exa Labs use AI agents to streamline onboarding, account management, and developer integrations. The examples are framed as practical patterns enterprise leaders can apply to turn workflows into repeatable operating capabilities.

• 7Ars Technica AI·8/27/2026framework_update
  Anthropic introduces a hardware standard for AI agents

  Anthropic has introduced the Model Hardware Standard (MHS), a research preview intended to give AI agents a common way to interface with and control physical devices. The company says the standard could reduce complex experimental integration work from weeks or months to hours or minutes by removing the need for custom translator software between components.

• 6Martin Fowler·8/27/2026framework_update
  Making Data Ready for Agentic AI

  The article argues that autonomous AI agents need data systems designed for machine consumption, not human analysts. It proposes a stack built around trusted data foundations, a context layer for meaning, and controlled access plus observability for governance and auditability.

• 5The New Stack·8/25/2026research
  How Security Teams Cut AI Costs Without Losing Capability

  The article argues that security teams can sharply reduce AI spending by using layered detection funnels, rules-based filters, and lightweight models before escalating only hard cases to more capable LLMs. It also notes that careful prompt design and richer context can improve accuracy while keeping expensive frontier-model usage limited to ambiguous cases.

• 3InfoQ·8/17/2026research
  Podcast on Agentic AI Risks in Software Ecosystems

  In this InfoQ podcast, Michael Stiefel speaks with Tracy Bannon about the role of artificial intelligence in software and the risks it can introduce across security, software development, and society. The discussion highlights that trust assumptions can break down when software ecosystems interact, increasing exposure at the boundaries between systems.

• 6OpenAI Blog·8/12/2026research
  OpenAI on how enterprises are moving from AI assistance to execution

  OpenAI says enterprises are increasingly adopting agentic AI to move beyond assistance and toward execution. The post highlights usage of ChatGPT and Codex, and argues that leading firms are gaining an adoption advantage ahead of others.

• 7The New Stack·8/9/2026research
  AI speeds up coding, but engineering output stays flat

  DX’s State of AI Impact in Engineering report says AI tooling has made individual developers faster, but organizational velocity has not improved in step. The report points to growing coordination overhead, flat innovation ratios, and weaker change confidence, especially in larger companies and larger pull requests.

• 4InfoQ·8/7/2026framework_update
  Rootly Ends Small PR Rule as AI Agent Code Generation Grows

  Rootly says its long-standing rule favoring small pull requests is no longer effective because AI agents now generate most of its code. The company is shifting review criteria toward blast radius, using feature flags and rollback readiness to judge risk instead of line count.

• 6InfoQ·7/27/2026framework_update
  AI Gateways as an Evolutionary Architecture Pattern

  The article argues that traditional API gateways are insufficient for agentic AI systems because they assume deterministic behavior and simple schemas. It proposes AI Gateways as a control-plane seam to centralize guardrails, model routing, agent identity, action policy, and semantic audit while keeping core platforms stable.

• 7NVIDIA GenAI·7/24/2026research
  NVIDIA and KAIST Launch Joint AI Research Lab in Seoul

  At the AI Summit in San Francisco, NVIDIA and KAIST announced a joint AI research lab at KAIST’s Kim Jaechul Graduate School of AI in Seoul. The lab will focus on agentic AI and combine NVIDIA’s full-stack AI stack, Nemotron open models, and partner cloud computing with KAIST’s research expertise.