Meta Muse vs Pine AI (2026)

Connectors reach email, calendars, the car and the home, Instagram, Facebook and Meta ads, each built with the service provider's API and paired with a skill that tells Muse how to use it well. Muse for Small Business adds Asana, Box, Canva, Dropbox, Figma, Granola, HighLevel, QuickBooks, Klaviyo, Lovable, Notion, Shopify, Slack, Stripe and Zoom. For anything else, Muse can write a custom connector of its own, and it calls tools through command line programs and skills it can build in its VM.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

Instead of connectors, Pine acts through its own phone calls, email and a computer of its own, and Pine Work needs no connector setup, integration map or handoff from tool to tool. It also dials into Zoom or Meet, follows the discussion, captures decisions and returns minutes and actions. Pine names no connector catalog or custom tool support.

SourcePine AI, 19pine.ai/work and pine.im/features/phoneread 2026-10-06

Muse breaks a goal into an actionable plan and runs several long tasks at once, launching swarms of subagents and handling concurrent subagents and cron jobs inside its VM. The model behind it was trained for long trajectories and multi agent coordination. A Goals tab tracks every goal it is pursuing and the plan to reach it, and a user can interrupt or pile on new tasks without waiting for the last one to finish.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

One agent carries a task through calls, emails, follow ups and computer steps, and tasks can be saved and reused. Batch jobs take a list and a goal and coordinate phone calls, browser tasks and email requests in parallel, up to more than 1,000 concurrent voice calls, keeping context for retries, recovering from failures automatically and flagging the exceptions that need the user. There is no workflow model with branching or deterministic steps for the customer to design.

SourcePine AI, pine.im/features/batch-jobs and 19pine.ai/workread 2026-10-06

Once a goal or task is set, Muse keeps working on a schedule and in response to relevant events, takes the next step and follows up while the app is closed. It messages first when background work turns up something new or needing a decision, holding proactive messages to a high bar by default, and the user can switch that off or dial it up or down. Requests come in through the iOS and Android apps and the web, all connecting straight to the user's own VM.

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Tasks in Pine Work can be saved, scheduled and reused, with recurring scheduled tasks shown such as a weekly Monday 9:00 industry brief. Inbound calls start work too, since Pine Phone answers, screens intent, routes the caller and escalates only what matters, and requests also come from the web, mobile and desktop apps and from MCP clients.

SourcePine AI, 19pine.ai/work and pine.im/features/phoneread 2026-10-06

Every file a user puts into Muse and everything it generates is stored on the user's own VM, the system of record for the account, and connectors bring in mail, calendars and business tools as tasks need them. For a business, connecting Facebook, Instagram and Meta ads means Muse starts out understanding the business and picks up its voice. Answers come from those files and live connections, with no separately managed knowledge base for a team to curate.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

Provider guides route a request to the right job, such as a cancellation or a refund with a particular company. There is no retrieval over the customer's own documents or knowledge base, and no way to load a company's own files, policies or records for the agent to draw on.

SourcePine AI, 19pine.ai/llms.txt and /workread 2026-10-06

Muse's memory persists across conversations and lives in files on the user's own VM, where the user can inspect, edit and download it alongside everything else Muse keeps. Durable application state sits in a separate database on the same machine. How long memory is kept, and whether a single memory can be removed without wiping the rest, is left unsaid.

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Pine Work turns a user's working context, routines and standards into a workflow Pine can run again and again, and conversation history is retained. On the computer, Safe Session Memory keeps a login so Pine can reuse the session for monthly statements, weekly exports and routine account chores, and batch jobs keep context for retries. Pine does not say what this memory covers, how long it lasts or how a user reviews and deletes it.

SourcePine AI, 19pine.ai/work, pine.im/features/computer and /features/batch-jobsread 2026-10-06

Every connector action passes through Sentinel, which checks it against the user's connector policy and allows it, denies it or stops for approval. Approval cards appear in the app's own interface, outside the conversation, with clear accept and reject actions, and grants are scoped to one connector and use, lasting once, for a session, for a task, for a set time or permanently. By default Muse browses freely and stops for anything hard to undo, such as writing an email or making a purchase, and the user can change that balance.

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Before actions with high impact, Pine shows the user what it plans to send, and the user can approve, edit, pause or take over at any time. On the computer it shows the plan before sensitive actions, and one click hands control back for a password, a 2FA prompt, a purchase or a final submission.

The user decides what Pine may do, users may need to approve actions or information a task needs, and calls use sensitive data only as needed, temporarily and with approval. In Pine Work the user can watch the agent's computer and take control at any time. There is no separate setting for which actions need approval, since the approval moments are built into how Pine works.

SourcePine AI, pine.im/features/phone, /features/computer and 19pine.ai/ai-information-pageread 2026-10-06

Each user's VM splits the agent's runtime cell, mapped to an unprivileged host user, from security services that run outside it.

Privilege separated workers hold connector credentials, an authentication service mints surrogate tokens so the agent never sees real ones, and Sentinel is the sole permission authority for connector actions and network egress, with least privilege enforced between processes.

Agentic red teaming and a public bug bounty back the design, paying up to $300,000 and up to $130,000 for a prompt injection that affects one user. Muse carries no SOC 2 report or other attestation, and a Confidential VM that would keep Meta itself out of user data is still with testers.

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SOC 2 work is in progress, and Pine invites contact about report availability, so no certification is held yet. Data is encrypted with TLS 1.3 in transit and AES-256 at rest.

Neither Pine staff nor its agents can see plaintext passwords, because models work with masked placeholders and the real credentials are supplied only when a task needs them, and Pine does not store or see full payment card numbers or keep call recordings.

Data is deleted within 30 days of a request, and neither Pine nor its AI providers train models on customer data. Pine names no roles, SSO or other access model for the customer's own organization.

SourcePine AI, 19pine.ai/security and /trust-center; 19pine.ai/trust-centerread 2026-10-06

A snippet under the Muse avatar shows what it is working on, and tapping the avatar opens its full activity log alongside every permission the user has approved. The Goals tab lists everything Muse is tracking and its plan to get there, and each approval card names the exact action and its purpose before it runs. The log and the permission history live inside the app, next to the settings that control them.

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The work stays visible in Pine Work, where the user sees the agent's computer and task progress, and conversation history is retained. Batch jobs bring together summaries, each task's status and supporting records, offer structured exports, and show retry progress and which jobs need the user's input. There is no trace or audit log of the agent's own steps.

SourcePine AI, 19pine.ai/work and pine.im/features/batch-jobsread 2026-10-06

Every user gets a dedicated, isolated VM in Meta's cloud, which holds their files, memory and credentials, and limited data leaves it for inference and telemetry. A user cannot choose where that VM runs, and there is no private or self hosted deployment.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

Pine runs as a hosted service reached through web, mobile and desktop apps for macOS and Windows. Production hosting is on Google Cloud in Iowa (us-central1), and transactional email runs on AWS in the US. There is no region choice, self hosting or private deployment.

SourcePine AI, 19pine.ai/trust-center/subprocessors and /workread 2026-10-06

Each connector ships with a skill Meta wrote on how to get the most out of it, and Muse for Small Business adds a named collection of skills and connectors for owners, covering content and ads, customer replies, cash flow and inventory. An Ideas tab and onboarding tips suggest what Muse could take on based on the user's goals. All of this shapes how the single agent works, and there are no separate prebuilt agents or task templates to pick from a catalog.

Sourcemuse.ai/businessread 2026-10-12

The product's named parts are Pine Phone, a voice assistant, Pine Computer, Pine Email, Pine Finance for tracking spending, batch jobs and Pine MCP. Pine also speaks to owner operated businesses such as cleaning, HVAC and remodeling companies, auto shops, pool companies and online shops, around converting demand, removing blockers and growing repeat revenue.

Consumer task categories (lower bills, cancel subscriptions, complaints, refunds) and provider specific guides route a request to the right job. All of these run on the same agent, with no separate agents or templates to adopt. Plans run from Plus at $99.90 a month billed annually through Max and Team to a custom Enterprise plan, with batch calling of up to 5, 10 or 20 calls by plan.

SourcePine AI, pine.im, 19pine.ai/llms.txt and /pricingread 2026-10-06

Meta trained the model at Muse's core itself, tuned for tool calling through command line programs and skills, long context and multi agent coordination, and a separate set of Meta safety models inspects inference. Conversations and tool trajectories are sanitized and used to train new checkpoints by default. A user cannot choose another model.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

Google's Gemini API handles the language and audio processing behind the agent, and Pine names no other model provider. Customers get no model choice.

SourcePine AI, 19pine.ai/trust-center/subprocessorsread 2026-10-06

Muse is reached through its own iOS, Android and web clients, which connect straight to the user's VM. Muse can extend itself by writing connectors and skills, but outside software has no API or MCP server through which to call it.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

The MCP server at https://mcp.pine.im/mcp is Pine's own. It signs in by OAuth (or an access token), and the tools in its repository, linked from pine.im, create, list, get and delete Pine sessions and tasks for calls, emails and computer work. Install guides cover Claude Code and Codex as plugins, Cursor from a clone of the repository, and other clients that support remote MCP with browser sign in.

SourcePine AI, pine.im/features/mcpread 2026-10-06

Meta hardened Muse through internal use, agentic red teaming and a bug bounty, and Muse judges whether a finished background result merits a notification before it pings the user. Both work on the vendor's product. A user has no way to test a goal, check the agent's output against cases or compare changes.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

By its own figures, Pine has a 93% success rate in negotiation, saves 270 minutes on average and has saved more than $3 million, and it promises twice the leads for local businesses. There is no evaluation harness, test case set or quality gate for a customer to run.

SourcePine AI, 19pine.ai/pricing and pine.imread 2026-10-06

Each user's Muse lives on a dedicated Linux VM with a browser, storage, CPU and memory, where it browses the web, fills forms, shops and books, and compiles code it writes. Its web activity appears as the user's own, and every network request it makes passes through Sentinel. Browsing is one of the headline capabilities, with critical steps such as a purchase held for approval.

Sourceresearch.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-museread 2026-10-12

Pine Work gives the agent a computer of its own that it operates to complete forms, research and move work across websites and software while the user watches, and consumer Pine uses a computer to log into service sites and submit forms. Pine Computer is a private cloud browser that navigates websites, fills forms, downloads files and finishes online chores, and batch jobs run more than 2,000 cloud browsers in parallel.

SourcePine AI, 19pine.ai/work, pine.im/features/computer and /features/batch-jobsread 2026-10-06