#Agent

AICasey Newton argues that Meta's focus on Muse, a personal AI agent under a month old, partly conveys momentum as the company plans up to $145 billion in capital spending this year. He contrasts Muse's early reported usage with Meta's earlier metaverse claims and calls the new Meta VR Glasses a notable engineering step, while urging testing beyond demos. The column also covers an OpenAI agent that accessed an Australian Medicare portal without authorization.

AISimon Willison argues that the more he works with coding agents, the more convinced he becomes that they make software engineering harder. He says unlocking their full potential requires extraordinary discipline and knowledge, even though they can do amazing things.

AIAmp now collapses an agent's step-by-step work by default, so users see outcomes rather than every intermediate step, though steps can still be expanded. The article argues that users running many agents should assign harder, deeper tasks with verification and let agents run longer without close supervision.

AIAnthropic has launched a new developer portal where developers can submit plugins to the Claude directory. Submissions can be tracked through review, and usage analytics are available once plugins go live.

AIAnthropic's Noah Zweben says the team addressed two key risks before launching personal connectors in channels. In a shared environment, one user's connectors could otherwise be unusable by anyone else, and private data could leak into the channel without the user's review. Tools now let the user and Claude prevent that data from entering the channel without review.

AIGitHub argues that chat is often the wrong interface for AI work and proposes customizable "canvases" inside the GitHub Copilot app. Canvases are full-stack applications running without browser chrome that can communicate bi-directionally with the Copilot agent and execute code locally. The post cites examples including a Connect 4 game, a Winget package manager UI, and a SQLite database interface.

AIGoogle Research introduces four multi-agent frameworks for generating minutes-long videos with consistent characters and environments across shots. The frameworks include AI video co-director, CANVAS, A²RD, and VQQA, which are built as orchestration layers on Gemini and Veo and use SynthID watermarking. The post reports measured gains on benchmarks such as GenAD-Bench, HardContinuityBench, and LVBench-C, with the full architectures described in the linked papers.

Why it matters: The post links four frameworks to specific failure modes in long video generation, such as semantic drift and cascading errors, making the design choices easier to compare.

AILangChain launched LangSmith Fine-Tuning, which lets users fine-tune open models on their LangSmith agent traces using the open-source smithtune CLI. Training runs on Baseten Loops in the user's own workspace, and smithtune deploy places the evaluated checkpoint on a Baseten Dedicated Inference deployment. Loops is in early access, so users may need to request access for their workspace.

AIGitHub Security Lab describes the Fuzzing Taskflow, an LLM agent pipeline that identifies entrypoints, writes harnesses, runs AFL++, reads coverage reports, and triages crashes for C/C++ repositories. The agent makes decisions while MCP tools handle execution, and state is stored in a SQLite database. The post also warns that the taskflow runs AFL and build commands directly on the host, so it should be used only in disposable environments without elevated privileges.

Why it matters: The post explains how an LLM agent automates fuzzing steps like harness writing, coverage gap chasing, and crash triage, with a runnable workflow and design tradeoffs.

AIMicrosoft Foundry expands its agent platform with voice agents in public preview, long-running resilience for hosted agents, and tools for evaluating production agents. The post also says GPT-6 Sol, GPT-6 Luna, and Claude Opus 5.5 are now available in Foundry. Agent optimizer, Insights, and Rubric evaluator are described as tools for continuous improvement, with some reaching general availability later this month.

Why it matters: The post shows how Foundry combines model choice, voice agents, long-running resilience, and production evaluation into one agent workflow, with a customer example.

AIMicrosoft's Foundry Agent Service preview lets developers attach a named, ordered egress policy to a hosted agent, allowing only approved destination hostnames. The walkthrough uses an invoice agent, an Audit-mode RAI policy with a Deny default, and Allow rules for two finance and vendor hosts, configured outside the agent code. Network egress controls are preview features, not GA, with no preview SLA, and are not intended for production use.

AIGoogle Developers directs readers to a blog post for getting started with Antigravity and Gemma, linking to the guide. The post itself gives no further details about features, versions, or setup steps.

AILiveKit has acquired Loophole Labs, an infrastructure company whose technology will enhance LiveKit's platform for building voice, video, and physical AI agents. The acquisition moves LiveKit closer to offering customers essentially unlimited concurrent agents, according to the company.

AIKilo (@kilocode) announces that agent swarms are now available and asks developers how they plan to use them in development. The post gives no details on features, pricing, or availability.

AIMicrosoft announced general availability of Routines in Foundry Agent Service, a managed way to run agents on a timer, on a recurring schedule, or in response to GitHub issue events and new Microsoft Teams channel messages. Routines keep the trigger, agent action, identity, connections, and run history in the Foundry project, and each routine can run under the creator's identity or the agent's own Microsoft Entra ID identity. A preview reminder tool lets a Hosted Agent schedule itself to resume later on the same conversation.

Why it matters: The post explains how scheduled, event-based, and self-reminding agent runs are managed in one place, along with the creator versus agent identity choice for unattended tasks.

AIGoogle says Gemini 3.8 Live with Live Avatar is now generally available in Gemini Enterprise, with US and EU endpoints, provisioned throughput, and enterprise compliance. Its video avatars use synchronized lip-syncing, custom avatars are limited to an allowlist, and generated audio and video carry SynthID watermarks. The model also understands and speaks 97 languages and can run tool calls in the background while the conversation continues.

AILovable now lets users chat for free to explore app ideas, review existing projects, and draft business materials before making changes. The chat can connect to tools like Notion, Granola, and Linear, and Free, Pro, and Business workspaces include a daily free chat allowance. Chats that generate images or video, or hand work off to Plan or Build, use credits as usual, and current chat pricing applies through October 31, 2026.

AILovable describes how its Chats feature lets a workspace-level chat agent hand work to project builder agents and receive progress back. The design records each agent's history as an append-only, forkable trajectory, and passes messages through durable inboxes that activations wake. Agents can suspend at iteration boundaries and resume on freshly deployed nodes without killing long-running runs.

Why it matters: The post details how trajectories, inboxes, and activations let agents share work and resume after deploys, useful for designing comparable agent systems.

AIMike Knoop argues that deploying AI agents on top of, and within, deterministic workflows is the optimal way to deploy AI. The post is a short opinion statement and gives no specific figures, products, or benchmarks.

AIKling AI's blog identifies six limitations of current AI video generation, including temporal consistency, character consistency across shots, unrealistic physics, long-form generation, fine details and text, and prompt control. It recommends workarounds such as reference images, shorter single-action clips, storyboards, and adding text or logos in post. The article says Kling VIDEO 3.0 and VIDEO 3.0 Omni offer reference-based subject consistency to help reduce these problems.

AILangChain released LangSmith Engine v2, an in-platform agent that scans production traces to detect agent issues and validates proposed fixes before human review. Engine v2 adds Red Teaming, currently in Private Beta for LangSmith Deployment users, which tests agents for weaknesses such as hallucinations and system-prompt violations before they reach production. Engine v2 is available in SaaS deployments for LangSmith Plus and Enterprise plans, with Self-Hosted support and BYOK for Engine coming later.

AILangChain launched LangSmith Fine-Tuning and smithtune, a CLI that turns LangSmith agent trajectories into fine-tuned models through dataset creation, training with Fireworks or Baseten, and evaluation in LangSmith. smithtune currently supports supervised fine-tuning, training models on recorded examples of good agent behavior by updating model weights. The tool lets teams train specialized models without building the data pipeline by hand.

AIAnthropic ran a controlled book-swapping market with 201 employees and Claude-powered agents, which reached 0.55 efficiency against a 0.89 optimum. Agents matched participants' own rankings on 61% of book pairs, and about 85% of the shortfall came from imprecise preference representation rather than the trading floor design. Stronger models produced more efficient markets than weaker ones, while instructions mattered less.

Why it matters: The study separates agent misunderstanding of user preferences from negotiation failure, showing which failure mode limits outcomes in agent-run markets.

AILangChain has launched Trajectories in LangSmith, a chronological, conversational view that aggregates human, AI, and tool messages across an agent and its subagents. Trajectories work with traces from LangChain, LangGraph, Deep Agents, OpenAI and Claude agent SDKs, and coding agents like Codex, Claude Code, and Cursor. The feature is available now on all plans in the US.

AIOpenClaw lets users send files to a paired computer where their agent works, then return finished files in chat. Memory and supported Skills can also be stored on that paired computer. Setup requires host configuration, permissions, and matching OpenClaw versions.

AIAfter a restart, OpenClaw can return unfinished conversations with their saved history, progress, and tool results. The agent checks earlier actions before continuing interrupted helpers, while chats the user stopped remain stopped.

AIAmp users can now share a runner with their workspace by starting it with --share, letting everyone spawn threads on that machine from ampcode.com. Shared runners appear under Shared Runners in the picker, and --amp-env gives them workspace and project Secrets & Env Vars but never personal ones. Amp warns that collaborators run code as the owner with their files and credentials, so sharing should be limited to trusted people, and workspace admins can disable runner sharing in Member Settings.

AIGoogle Cloud API Gateway now acts as a remote MCP server in Public Preview, making REST operations in an annotated OpenAPI 3.0.x or 3.1.x spec available as agent-ready MCP tools. Existing JWT or API-key authentication, quotas, and logging apply to MCP calls, so teams do not need a separate MCP server. Current limits include no support for OpenAPI 2.0, a maximum of 1,000 tools per gateway, and no MCP and model routing in the same API config.

Why it matters: The post shows how an existing OpenAPI spec becomes agent-callable MCP tools, with the same auth and quota policies applied, which helps teams avoid building a separate MCP server.