Meta released Muse Wonder through the Meta Model API and Muse Code, with a one-million-token context window and listed API pricing of $4 per million input tokens and $20 per million output tokens. The larger Muse Wonder Full is available only to Muse Code subscribers. Meta positions both models for coding and sustained personal and professional agent tasks with Active Memory and Active Focus.
Why it matters: This is a new hosted frontier-model choice for long-running agents rather than a research preview. Its cross-conversation memory, browser and application work make model quality, retained personal context, tool authority and cost part of one deployment…
Meta began a US rollout of Muse on iOS, Android and the web. The agent uses a dedicated Muse Secure VM, can browse, complete forms, email, book travel and purchase, and requests approval for sensitive actions.
Why it matters: A mass-market agent that keeps working after the app closes moves persistent memory, credentials and real-world transactions into one consumer control plane, making permission and recovery behavior consequential.
HarnessSafe executes 328 persistent-carrier attacks across memory, skills, tools, subagents, summaries, and shared artifacts, finding large containment differences for the same model across harnesses and inference backends.
Why it matters: Agent security cannot be inferred from a model name when the surrounding harness and serving path change whether malicious state persists or executes.
MirageBench judged 143,616 claims across 12 models and found 35–49% exceeded the available evidence; model self-reports did not reliably identify the least over-inference, and unsupported attributes accumulated in a multi-turn pilot.
Why it matters: Persistent personalization can turn plausible stereotypes and guesses into durable product state that shapes later recommendations, eligibility, and communication.
AWS released Kiro Crew as an Apache-2.0 workspace that runs locally or on user-controlled remote hardware, coordinates parallel Kiro agents, preserves memory across sessions, and runs scheduled or long-lived engineering work.
Why it matters: It moves coding agents from supervised, single-session prompts toward inspectable background work with persistent state, approvals, security policy, and audit trails in one self-hosted control plane.
Nous Research released Hermes Agent under an MIT license as an agent that can run on a user's server, maintain persistent memory, generate skills, schedule unattended work and delegate to isolated sub-agents. It supports messaging surfaces, browser and search tools, multiple models, and local, Docker, SSH, Singularity or Modal sandboxes.
Why it matters: This makes a persistent, user-controlled agent stack inspectable and deployable outside a single vendor's hosted assistant. Memory, scheduling and sandbox choice are useful primitives for building a personal LKB without handing every interaction to one…
Anthropic and Andon Labs let Claude Sonnet 3.7 manage an office shop for about a month using search, notes, customer chat and delegated physical labor. It sourced products and resisted some jailbreaks but lost money through weak pricing, discounts, memory failures and hallucinations.
Why it matters: Long-running economic tasks expose compounding errors and state-management failures that short agent benchmarks rarely reveal.