#Agent

AIThe Claude Blog published a guide to building scheduled agent automations with Claude Managed Agents (beta) that reads custom sources such as Slack and GitHub and posts a daily brief. The guide covers scoped vault credentials, per-source bookmarks so no window is lost or repeated, and confirming each Slack post before updating records. It also covers read-only access, a per-run spending cap, and a reference implementation with a Claude Code setup command.

AISophos says agents built through OpenAI Daybreak, combined with its cybersecurity expertise, cut average response time from 38 minutes to 89 seconds for cases handled by those agents. The company says the agents help its MDR team investigate threats faster and protect customers at scale while keeping human judgment central.

AISnyk moved its internal support agent, Snyk Assist, into the core Snyk product in September 2026, giving every paying customer access. Built on LangChain and LangGraph with observability in LangSmith, the agent answers questions in plain language and can open support cases or log feature requests. It runs as a single agent behind Slack, web and API surfaces, with tools attached per user permissions.

AITessl's blog post argues that teams should record agent mistakes as attributed, signed diary entries, then curate them into reusable context packs rather than adding unverified rules to files like AGENTS.md. The author describes a REST API case where an agent regenerated the OpenAPI spec and TypeScript client but missed the Go client, and the same lesson had to be re-taught in a fresh session.

AITessl argues that an AI-native organization collapses the handoff between people who own outcomes and the work itself, so product managers and designers can execute changes through agents. It says PR count and token spend are insufficient measures, and that merge rate better shows whether the new workflow is working. The article also says the boundary should follow decision authority, with engineers still owning architecture and data models.

AIThe author argues that system use cases, with actors, preconditions, scenarios, and acceptance criteria, work better than user stories as the input for AI code generation in enterprise business applications. He describes a process that skips the plan-and-task phase, reverse-engineers legacy systems into use cases and entity models for modernization, and recommends self-contained system verticals and risk-based review.

AIAI DevCon New York, running November 2–4 at Industry City in Brooklyn, centers its program on software factories, the systems needed to make agentic development repeatable, trustworthy and scalable. The article argues that moving from one developer using an agent to an engineering organization requires layers covering context and skills, harnesses and tools, orchestration, verification and evaluation, and feedback.

AIThe article explains how a software factory combines agents, tools, and engineering practices to take work from intent to verified output. It argues that teams should enforce domain rules, workflow gates, and tool permissions independently, version shared guidance and checks, and review lessons before reuse.

AIOpenAI's DevDay 2026 session demonstrates Codex shifting from a single-user tool to a team-oriented agent. The session shows a persistent personal agent investigating a 2am outage, from the first Slack message through a reviewed fix, using voice, Appshots, plugins, and meeting notes to keep the team informed.

AIOpenAI's DevDay 2026 video presents what's new in Codex across the CLI, app, and web. Live demos show moving from delegating individual tasks to having Codex tackle entire problems and goals. The source is a short description only, so no specific features or figures are confirmed.

AIOpenAI's DevDay 2026 session shows how its teams use ChatGPT to manage launches, research competitors, and turn expertise into tools. The source provides no further details on specific features, metrics, or availability.

AIA DevDay 2026 session from Higgsfield explores how its agents choose models and provision their own compute. It covers when to use dedicated GPUs versus hosted models, how to deliver predictable throughput, and where the approach can fail.

AIStack Overflow's small teams used Codex to build Stack Overflow for Agents and reimagine Stack Internal, according to a DevDay 2026 session. The talk covers lessons for accelerating R&D and bringing new products to market.

AIOpenAI's YouTube video shows developers how to build plugins for ChatGPT that run a custom interface alongside a conversation. The plugins can bring selected context into the user's work with ChatGPT, according to the source's description of plugin extensions through Meetings and OpenAI Developers.

AICornerstone OnDemand built Orion AI, a multi-agent system using Amazon Bedrock and the open source Strands Agents framework, that cut database diagnosis from 45 minutes to 10, a 78% reduction. The system also reduced manual lifecycle steps from more than 10 to a single interaction and filtered redundant alerts by a median of 65%. A three-person team delivered it in six months.

AIXiaomi released the MiMo-V2.5-TTS Series, three speech synthesis models for stock voices, voice design, and voice cloning. The models accept natural-language style instructions and inline audio tags, and the source says the three models are free of charge for a limited time on the Xiaomi MiMo API platform. Xiaomi also open-sourced integration Skills for agent applications on GitHub.

Why it matters: The release shows how a TTS family adds style instructions, inline audio tags, and voice design or cloning to speech synthesis, which matters for agent and creative workflows.

AIXiaomi MiMo-V2.6-Pro is applied to new materials R&D across literature review, molecular design, and automated dry-lab experiments for PFAS capture. The source offers no benchmark scores, speed, pricing, or availability details.

AIDatabricks introduces database branching in Lakebase Postgres, letting each coding agent work in its own isolated database branch created in under a second regardless of size. Branches use copy-on-write storage, consuming extra space only as they diverge, and scale to zero when idle so unused branches incur no compute cost. Schema changes are tracked in code and promoted to the parent branch through migrations rather than merged back, and ephemeral branches are created per pull request for testing.

AIOpenAI's YouTube livestream presents three demos showing how Codex supports production monitoring. The demos cover tracing a checkout failure in Grafana, investigating a Kubernetes rollout causing out-of-memory restarts, and linking a Codex Security finding about a missing resource limit to service availability.

AINVIDIA developers are pairing frontier AI models, including GPT-6 Astra and Claude Fable 5, with Omniverse libraries to turn simulation ideas into working applications. Examples include a humanoid warehouse simulator, an autonomous-driving testing workflow, and sensor-matching digital twins. The projects are guided through natural-language instructions and reviewed by developers.

AIDevelopers are pairing frontier AI models with NVIDIA Omniverse libraries to turn simulation ideas into working applications, from humanoid warehouse simulators to autonomous-driving test environments. In the examples, developers direct AI agents through natural-language instructions and review results, while Omniverse provides GPU-accelerated physics, rendering and sensor simulation. One experiment reported a simulated Unitree G1 humanoid clearing a hurdle in 64 of 100 trials.

AINVIDIA's Omniverse libraries, guided by SimReady Foundation specifications and agentic NVIDIA skills, provide a structured workflow for converting CAD assets to OpenUSD for robotics simulation. The workflow covers configuring and validating materials, collision geometry, joints, and other physics properties before testing robot behavior.

AIClaude Code v2.1.295 adds onFailure: "block" for command and HTTP hooks, so a hook that cannot start, times out, or exits unexpectedly blocks the action. The release also adds an optional models list for Claude apps gateway upstreams, plus upstream_request_id in the inference audit event, and fixes a range of MCP, plugin, and terminal issues.

AIOpenAI's Codex 0.162.0 release adds tools for creating and listing managed Git worktrees from trusted local projects when the worktrees feature is enabled. The update also lets users pin tasks in the agent Command Center, copy transcript blocks with /copy, and make URLs clickable in approval headers, questions, and warnings, along with several Linux and Windows sandbox fixes.

AILeapfrog, a small team doing high-volume AI visual and production work for fashion and brand clients, is building a "one brain" system that makes company knowledge and client context searchable through natural-language agents. The starter stack described is OpenClaw in a sandbox, a GitHub repository, Obsidian on the local machine, and Telegram as the access point. The system's research structure had roughly 1,200 files at the time of the talk.

AITessl's talk at AI Native DevCon London argues that agent skills, which can be markdown files with instructions and bundled material, act as supply chain components that can shape agent behavior. The author says reading SKILL.md once is insufficient because risks can sit in supporting files, updates, and workspace trust settings. He identifies the danger as the combination of private context, untrusted content, and external communication, and cites research scanning roughly 4,000 public skills for issues including malware-like behavior.

AIMozilla.ai's cq project proposes a shared knowledge layer where AI agents capture lessons from non-obvious fixes as structured knowledge units that other agents can later query. The default setup is local-first, using a local SQLite database so nothing leaves the machine, with an option to connect to a remote team server that adds review.

AIJohn Groetzinger, writing in a personal capacity rather than for Cisco, argues that enterprise skills need packaging, evaluation, syncing, and distribution rather than scattered markdown files. He describes using skills to make cheaper models viable, converting curated TAC knowledge-base articles into maintained skills, and rolling out an eval framework across teams. He also describes syncing a repository README to Confluence with a deterministic script.

AIAmazon Bedrock AgentCore payments lets AI agents pay for model inference one request at a time, using x402 with USDC on the Base network. Incarna used the service to connect its agents to BlockRun, a pay-as-you-go router serving more than 90 models from more than 15 providers. Spending limits are enforced at the infrastructure layer, outside the model.

AIThe NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition with a system built around a smaller, clearer, and easier-to-verify agent harness. The competition required agents to answer natural-language questions over heterogeneous sources, including databases, CSV and JSON files, prose documents, PDFs, and briefing videos.

AITessl argues AI code review is slow because generated code outpaces trust, and proposes executable specs that let agents check preview environments against product intent. Its spec reviewer splits work between a planner agent that extracts requirements and parallel verifier agents that test each one against the code and base branch.

AINVIDIA Dynamo uses a unified session-level identifier to make its inference stack aware of agent sessions, subagents, and their KV cache across turns and tool calls. On SWE-bench, two TP4 MiniMax-M2 replicas on one 8xH100 node gained roughly 12-16% throughput from program-aware scheduling over KV-aware routing alone. The post also describes experimental shared-pool indexing and a proposed KvHint interface for session-aware cache policies in vLLM and SGLang.

Why it matters: The post explains how session identifiers let an inference stack track agent working sets, with measured throughput gains on SWE-bench and agentic RL rollouts.

AITessl's blog post argues that repository automation needs Continuous AI, a third pillar alongside CI and CD for scheduled, auditable AI workflows that improve repositories over time. The article describes GitHub Agentic Workflows, which harden agentic workflow specifications into GitHub Actions that can run coding agents such as Claude Code, Copilot CLI, Gemini CLI, or Codex-style agents. It emphasizes read-only agent steps, restricted outputs, and human review of pull requests.

AIAWS published a reference architecture for running multiple teams on one Amazon SageMaker HyperPod EKS cluster, with each team isolated in its own Kubernetes namespace. The design combines AWS IAM Identity Center for authentication, per-team SageMaker AI domains, HyperPod Task Governance for fair resource allocation, and namespace-level cost allocation for per-team spend visibility.

AIGoodfire Research describes probe-based cyber monitors for Kimi K3 and GLM 5.3 deployed on a production inference stack. The probe filters suspicious exchanges before an LLM judge reviews them, reaching about 93% recall at a 5.5% benign-session interruption rate at roughly 50x lower judge cost. In FAR.AI's red-teaming, the monitor reduced universal jailbreaks to zero across 140 tested strategies.

AIOracle is using ChatGPT Work and Codex to turn specialist knowledge into fast, repeatable workflows across recruiting, engineering, and operations. The source does not provide figures, timelines, or specific results beyond the headline's claim that days of work can take minutes.

AIAugment Code is selling select assets, including Cosmos, Auggie CLI, and the Code Context Engine, to Harness, and the product team is moving to Harness. The company says Harness's integrated platform delivers these capabilities to customers more effectively than building them independently. Harness describes itself as building the Autonomous SDLC Platform for shipping AI-written code across enterprises.

Why it matters: The announcement shows how a coding AI company is folding its products into a larger software delivery platform, a shift that shapes how enterprise teams will buy these tools.

AIThe author argues that agents working across a company fail because they lack the decisions and context recorded in threads, meetings, and DMs, not because the model is weak. The approach stores distilled claims with source evidence and time, never overwrites facts, labels missing information explicitly, and resolves permissions before the model runs. The report cites results on LongMemEval, including 99.8% top-ten evidence recall and $8.24 ingestion cost, and says an open-weight model can match frontier extraction quality.

AIReplit and Databricks integration, now generally available with native Lakebase support, lets enterprise teams build apps from plain-language prompts using Replit Agent and deploy them as Databricks Apps. Deployed apps inherit automatic user authentication and Unity Catalog access controls, and Replit Agent auto-provisions a managed Lakebase Postgres database for operational data. Lakebase keeps app-written data inside the Databricks perimeter instead of a separate external database.

AIJetBrains released Mellum2.1, a 12B mixture-of-experts model with 2.5B active parameters under the Apache 2.0 license, built for coding agents. Post-training shifted to reinforcement learning across thousands of environments and millions of sandboxed runs, and the model is available on Hugging Face. The source reports gains over Mellum2 on LiveCodeBench, AIME, GPQA Diamond, BFCL v4, IFEval, and SWE-bench Verified, and says it serves almost twice the tokens of Qwen3.5-9B under heavy load.

Why it matters: The post shows how reinforcement learning in real sandboxed environments changed a compact open model's repository work, with benchmark gains against Mellum2 and two peers.