Perplexity Teammates: The Complete Guide for Agentic AI Architects — Always-On Agents That Own Your Ongoing Work (2026)

A marketing manager at a mid-size SaaS company spends four hours every Monday triaging campaign performance across Google Ads, HubSpot, and Salesforce — pulling numbers, cross-referencing attribution, drafting adjustment recommendations. By Tuesday, the window to act on weekend data has already closed. Now imagine that workflow delegated to an AI agent that runs continuously: monitoring campaigns overnight, flagging underperformers before the team’s morning standup, and drafting reallocation proposals grounded in real-time data. That’s the promise Perplexity shipped on November 4, 2026, when it launched Teammates — persistent, always-on AI agents built on top of its multi-model Computer platform.

Teammates represent a fundamental architectural shift in how AI agents operate. Instead of the familiar prompt-response loop where you invoke an agent, wait for output, and close the session, Teammates maintain ongoing ownership of work across days and weeks. They remember project context, monitor for changes proactively, hand off tasks to other Teammates, and message your human colleagues in Slack — all without a fresh prompt.

For agentic AI architects and RPA professionals, this matters because Perplexity isn’t alone. Anthropic launched Claude Duet as a research preview on October 21. OpenAI unveiled Dots at DevDay on September 29. SpaceXAI’s Grok Bot has been in beta since August. Meta shipped Muse on September 8. The always-on agent category has gone from concept to five-vendor competitive market in under 90 days — and the architectural decisions being made now will define enterprise AI workflows for years.

This guide breaks down exactly how Perplexity Teammates works, how its architecture compares to the competition, what it means for existing RPA and automation pipelines, and how to evaluate whether it belongs in your stack.

Table of Contents

• What Are Perplexity Teammates?
• Architecture: How Teammates Works Under the Hood
• Key Features Deep Dive
• Setting Up Your First Teammate: Step-by-Step
• Pricing, Plans, and Credit Economics
• Competitive Landscape: Teammates vs Claude Duet vs Dots vs Grok Bot vs Muse
• What This Means for RPA and Automation Teams
• Enterprise Use Cases: Where Teammates Fits
• Security, Governance, and Compliance Considerations
• Current Limitations and What’s Coming
• The Verdict: Who Should Use Perplexity Teammates
• FAQ
• Key Takeaways
• References

What Are Perplexity Teammates?

Perplexity Teammates are persistent AI agents designed for complex, ongoing work — not one-off tasks. Launched on November 4, 2026, they represent the next evolution of Perplexity’s Computer platform, which debuted on February 25, 2026, as a multi-model orchestration system capable of running workflows that span hours, days, or weeks.

The core concept: instead of prompting an AI assistant each time you need something done, you assign a Teammate to own a workstream. A Teammate plans and coordinates projects, switches priorities as conditions change, presents results for your review, and acts proactively when it detects something that needs attention. Perplexity describes the paradigm shift as moving “from writing prompts in a session to giving high-level goals,” according to CEO Aravind Srinivas.

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RPA Center of Excellence: Building and Sustaining Success

Each Teammate runs in its own isolated environment with a separate browser session. It inherits the full capability set of Perplexity Computer — including its multi-model orchestration engine, 400+ app integrations, Projects (persistent research and context), Skills (reusable workflows), and Memory (cross-session learning). But where Computer handles discrete tasks, Teammates maintain continuity: they remember previous decisions, learn from feedback, and adjust their approach over time.

Johnny Ho, Perplexity’s product lead, describes the current form factor as a “research preview” — an important qualifier that signals the product is still evolving rapidly. The initial use cases highlighted include deep research, data analysis, QA, ticket triaging, and code review.

Architecture: How Teammates Works Under the Hood

The Multi-Model Orchestration Layer

Perplexity Computer — the runtime foundation beneath Teammates — is a multi-model orchestration system that routes work across 19+ AI models based on the nature of each subtask. The known model roster includes:

Perplexity CBO Dmitry Shevelenko has described Computer as a “massively multi-model orchestration system.” When you give a Teammate a project — say, building a competitive analysis dashboard — it decomposes the goal into subtasks and spawns specialized sub-agents for each: one for web research, one for data extraction, one for visualization, one for drafting the narrative. These sub-agents run in parallel and asynchronously, with human checkpoints when input is needed.

This is architecturally distinct from single-model agent frameworks like LangGraph or CrewAI, where you pick one LLM and build your orchestration logic around it. Perplexity’s approach is closer to what a large enterprise integration team would design: route each task to the model best suited for it, manage the state across all of them, and present a unified result.

Persistent State and Memory

The critical architectural difference between Teammates and standard agent sessions is persistence. A Teammate maintains:

• Project goals and context — the high-level objectives and constraints you set during onboarding
• Task history — what it has done, what worked, what didn’t
• Shared files and artifacts — documents, spreadsheets, and outputs from previous runs
• Learned preferences — feedback you provide is applied to future tasks (for example, an operations Teammate that learns your agency partner has a two-week lead time factors that into all future deadline calculations)

This memory persists across sessions and workstreams, which solves one of the fundamental limitations of traditional RPA bots and single-session AI agents: the cold-start problem. Every time you invoke a conventional agent, it starts from zero context. Teammates start from where they left off.

Teammate-to-Teammate Communication

Perplexity has built inter-agent handoff directly into the Teammates architecture. One Teammate can pass findings to another — for example, a QA Teammate that discovers website bugs can hand its findings to a development Teammate to prepare fixes. This multi-agent coordination happens within the Perplexity platform without requiring external orchestration tools.

For architects accustomed to building multi-agent pipelines with frameworks like LangGraph or AutoGen, this is a significant convenience layer — though it comes with the trade-off of being locked into Perplexity’s orchestration model rather than defining your own agent communication protocols.

Integration Architecture

Teammates inherits Perplexity Computer’s 400+ app integrations. At setup, you connect “plugins” — the named examples include Slack, GitHub, and Gmail, with repository-level scoping for GitHub. The broader Computer platform has been reported to support Gmail, Outlook, GitHub, Linear, Slack, Notion, Snowflake, Databricks, and Salesforce, among others.

The Slack integration deserves special attention: you can add a Teammate to a Slack channel, allowing human team members to hand it work or ask for status updates directly from their existing communication flow. This is the bridge between AI agent work and human team coordination that enterprise deployments consistently identify as a critical adoption factor.

Key Features Deep Dive

1. Ongoing Project Ownership

The headline capability: delegate an ongoing piece of work, and the Teammate takes ownership. It plans the approach, executes against it, checks results along the way, and returns completed work for review. Perplexity’s examples include investigating product issues, researching a new market, and testing product improvements — all tasks that span multiple sessions and require maintaining context across days or weeks.

This is fundamentally different from how most automation is built today. A typical RPA bot executes a defined process: extract data from system A, transform it, load it into system B. A Teammate operates at the goal level: “keep our ad spend optimized” or “make sure vendor deadlines don’t slip.” The agent decides how to achieve the goal, not just what steps to run.

2. Proactive Monitoring

Teammates don’t wait for prompts. They monitor their domain and act when they detect changes or anomalies. Perplexity’s launch post gives a sales example: a Teammate notices that a regular customer has stopped buying, reviews their order history and support tickets, then either drafts a retention offer or flags unresolved issues that need human attention first.

This proactive behavior is the feature that most directly challenges traditional RPA scheduled triggers and event-driven automation. Instead of building explicit event listeners and conditional logic, you describe the outcome you want monitored and the Teammate handles the detection and response logic.

3. Cross-Session Memory and Learning

Every interaction and outcome feeds into the Teammate’s memory. Feedback you provide — “this report format works better” or “always check with legal before sending external communications” — is stored and applied to future tasks. The memory spans workstreams, so a marketing Teammate that learns your brand voice guidelines applies them across social posts, email campaigns, and ad copy without being re-instructed each time.

4. Slack-Native Team Collaboration

Adding a Teammate to a Slack channel transforms it from a personal agent into a team resource. Colleagues can assign it tasks, check on progress, or ask questions — without needing their own Perplexity Max subscription. This lowers the adoption barrier significantly and maps to how enterprise teams already coordinate work.

5. Human-in-the-Loop Controls

Despite the autonomous operation, Teammates are designed with approval gates. They share progress updates and send approval requests at decision points. Users can inspect completed work, take over the desktop environment directly, or redirect a Teammate mid-task. This human oversight model is critical for enterprise adoption, where fully autonomous operation without review gates is typically a non-starter for compliance and risk teams.

6. Perplexity Computer Foundation

Teammates inherits everything from Computer: Projects (persistent research and organizational context), Skills (reusable workflows like an outreach-writing skill or a data-cleaning pipeline), Memory, and weekly Automations (scheduled Computer sessions for recurring tasks like a pipeline review). The research mode alone runs seven parallel search types: web, academic, people, image, video, shopping, and social.

Setting Up Your First Teammate: Step-by-Step

Perplexity outlines a five-step setup process:

1. Sign in on the web and select Teammates in the left navigation panel.
2. Name the Teammate after the project or workstream it will handle (e.g., “Campaign Performance Monitor” or “Vendor Compliance Tracker”), then follow the guided setup steps.
3. Connect tools by adding plugins — Slack, GitHub, Gmail, and others. For GitHub, choose which specific repositories the Teammate can access (repository-level scoping).
4. Describe the work it owns — where the work comes from, what it should deliver, and when it should act. This is the goal-definition step that replaces traditional process documentation.
5. Assign it to a Project and give it an initial task (e.g., “Research the competitive landscape for our Q1 product launch” or “Monitor and triage all P1 support tickets in the #escalations channel”).

The setup is designed to leverage existing Perplexity infrastructure: if you’ve already built Projects, Skills, or Memory in Perplexity Computer, your Teammates inherit that context immediately. As Srinivas noted, “existing connectors, files, and sessions carry over.”

Pricing, Plans, and Credit Economics

Teammates is available exclusively to Perplexity Max subscribers:

The credit system is important to understand. Each Computer task and Teammate action consumes credits from the 10,000 monthly allotment. Standard Perplexity searches remain unlimited and don’t consume credits. When credits run out, active tasks pause rather than cancel, resuming when credits become available again. Auto-refill and spending limits are configurable.

For an always-on Teammate monitoring multiple workstreams, credit burn rate will be the critical cost variable. Perplexity hasn’t published granular credit-per-action pricing, so architects should plan to monitor usage closely during the initial deployment and establish baseline consumption patterns before scaling.

Competitive Landscape: Teammates vs Claude Duet vs Dots vs Grok Bot vs Muse

The always-on AI agent market consolidated from concept to multi-vendor reality in Q3-Q4 2026. Here’s how the five major players compare as of November 2026:

Key Differentiation Points

Perplexity’s multi-model advantage: While competitors lock you into a single model family, Perplexity routes tasks to whichever model handles them best — Claude Opus 4.6 for reasoning, Gemini for research, ChatGPT 5.2 for long-context work, Grok for speed. This architectural decision means Perplexity benefits from advances across all model providers simultaneously, though it also means it depends on API access agreements with competitors. For architects evaluating lock-in risk, this is a double-edged sword.

Claude Duet’s connector depth: Anthropic’s 6,000+ connectors and plugins — including Gmail, Slack, QuickBooks, Gusto, and Shopify — give it the broadest integration surface of any always-on agent. Its security model is also notably conservative: Duet never sees passwords or sign-in tokens, and a classifier blocks sharing of card or government ID information. For regulated industries, this matters.

OpenAI Dots’ ecosystem leverage: Dots integrates tightly with Codex for coding tasks and ChatGPT’s 4,000+ plugin ecosystem. Its “one Linux computer and Chrome browser” architecture gives each dot a self-contained execution environment. At $100/month through ChatGPT Pro, it’s priced between Duet’s entry point and Teammates’ premium tier.

Grok Bot’s team-native design: With support for up to approximately 50 bots per user, Team Bots on Enterprise plans, and the ability to record screen sessions and draft reusable skills from them, Grok Bot is the most team-oriented option — though its shared-computer-per-user architecture and undocumented memory model are concerns.

Meta Muse’s consumer play: With a free tier and WhatsApp integration, Muse targets personal errands and household management — booking tickets, managing shopping, coordinating services. It’s not competing for the enterprise use case, but its Spaces feature (collaborative workspaces for agent interaction) hints at where the consumer segment is heading.

The Open-Source Alternative

It’s worth noting that Hermes Agent (250,000+ GitHub stars) and OpenClaw (390,000+ GitHub stars) offer self-hosted always-on agent capabilities with full model choice, editable memory, and support for 20+ messaging platforms. The trade-off is setup complexity and infrastructure management. OpenClaw’s heartbeat mechanism — which wakes the agent every 30 minutes by default — can accumulate significant token costs on large context histories. But for organizations that need data sovereignty or custom model selection, the open-source path remains viable.

What This Means for RPA and Automation Teams

The emergence of always-on AI agents like Teammates represents the most significant architectural challenge to traditional RPA since the technology’s inception. Here’s why RPA architects need to pay attention:

From Process Automation to Goal Automation

Traditional RPA operates at the process level: define a sequence of steps, encode them in a bot, trigger execution on a schedule or event. The bot does exactly what it’s told, nothing more. Teammates operates at the goal level: describe what you want achieved, and the agent figures out the how. This is the gap between UiPath’s “do steps 1-17 to process this invoice” and a Teammate’s “keep our AP clean and vendors paid on time.”

This doesn’t mean RPA is obsolete — far from it. Deterministic, high-volume, rule-based processes (invoice processing, data migration, compliance reporting) still need the predictability and auditability that RPA provides. But the layer above those processes — the coordination, exception handling, monitoring, and decision-making that currently falls to human operators — is exactly where always-on agents excel.

The Hybrid Architecture Emerging

The practical architecture for most enterprises will be hybrid: RPA bots handle the deterministic execution layer, while AI agents like Teammates manage the orchestration and judgment layer. A Teammate might monitor a process dashboard, detect that invoice exception rates have spiked, investigate the root cause by querying the ERP and reviewing recent vendor communications, then either fix the issue by adjusting bot parameters or escalate to a human with a recommended action — all proactively, without a prompt.

For RPA architects, the integration question is: how do your existing bots expose status and accept instructions from an AI agent layer? If your automation estate is built on modern platforms with API-accessible orchestrators (UiPath Orchestrator, Automation Anywhere Control Room), the integration path is relatively straightforward. If it’s a legacy estate with headless bots and no API surface, the integration cost could be substantial.

Skill Reuse and Migration

Perplexity’s Skills concept maps loosely to RPA’s reusable components and libraries. An outreach-writing Skill in Perplexity is conceptually similar to a reusable RPA component for email template processing. The difference is that Skills are learned and refined by the agent over time, rather than manually maintained by developers. For teams evaluating migration paths, the question is which processes benefit from this adaptive behavior and which need the deterministic guarantees of coded automation.

Enterprise Use Cases: Where Teammates Fits

Based on Perplexity’s launch documentation and the capabilities of the underlying Computer platform, here are the use cases where Teammates is most likely to deliver value:

Marketing Operations

Managing social media calendars, monitoring campaign performance across platforms, drafting content adjustments when metrics slip, tracking competitor promotions, and coordinating ad spend reallocation. The proactive monitoring feature is particularly valuable here — a marketing Teammate can catch a campaign underperforming before the weekly review meeting.

Sales Operations

Identifying opportunities in customer inquiry patterns, preparing outreach materials, following up on open conversations, and flagging support issues that need resolution before a sales conversation can proceed. The cross-session memory means the Teammate builds an increasingly detailed understanding of each account over time.

Customer Support Escalation

Reviewing daily complaint volumes, identifying trends, flagging cases that warrant executive attention, drafting responses, and tracking resolution to completion. This is a natural fit for the Teammate model because support escalation requires maintaining context across multiple interactions and stakeholders over days or weeks.

Vendor and Project Management

Tracking deadlines, checklists, vendor deliverables, and paperwork. Proactively following up when a deadline is approaching and required items are still outstanding. The operations use case is one of Perplexity’s highlighted examples: a Teammate that knows your agency partner’s two-week lead time and factors it into every deadline calculation.

Research and Competitive Intelligence

Continuous monitoring of competitor activity, market trends, regulatory changes, and technology developments relevant to a specific product or business unit. The seven-channel parallel research mode (web, academic, people, image, video, shopping, social) gives Teammates broader research coverage than most single-tool solutions.

Security, Governance, and Compliance Considerations

For enterprise architects, the security model is often the deciding factor. Here’s what we know about Teammates’ security posture:

Environment Isolation: Each Teammate runs in its own isolated environment with separate browser sessions. This means credential leakage between Teammates is architecturally prevented — a Teammate managing your GitHub repos can’t access another Teammate’s Salesforce credentials.

Credential Handling: Perplexity states that saved credentials are “encrypted and entered securely.” The specific encryption standards, key management practices, and storage locations are not publicly documented as of launch.

Human Oversight: The approval-gate model means Teammates surface decisions for human review at configured checkpoints. Users can inspect work, take over the desktop, or redirect mid-task. This is the right pattern for progressive trust building — start with tight approval gates and loosen them as confidence grows.

Tool Scoping: The setup process includes scoping which resources a Teammate can access — for example, choosing specific GitHub repositories rather than granting blanket access. This principle-of-least-privilege approach is essential for enterprise deployment.

What’s Missing: As of launch, Perplexity has not published details on SOC 2 compliance, GDPR data processing agreements, data residency options, audit logging granularity, or admin controls for team-wide Teammate governance. These gaps will need to be addressed before most regulated enterprises can move beyond pilot deployments. The Perplexity Trust Center is the resource to monitor for updates.

Current Limitations and What’s Coming

Teammates launched as a “research preview,” and several limitations are worth noting:

• Web-only at launch: No mobile app support yet. iOS is described as “coming soon,” with no Android timeline disclosed. For professionals who need to monitor and redirect agents from their phone, this is a meaningful gap — especially since Claude Duet launched with iOS and Android support from day one.
• Max-only pricing: At $200/month, the entry price excludes individual practitioners, students, and small teams. There’s no tiered access — Free and Pro users must upgrade fully.
• Credit opacity: Perplexity hasn’t published granular credit consumption rates per action type, making cost forecasting difficult for enterprises evaluating at scale.
• Limited integration documentation: While the platform claims 400+ integrations, the Teammates-specific documentation only names Slack, GitHub, and Gmail as plugins. The full integration surface and its capabilities within Teammates (vs. Computer generally) need clarification.
• No enterprise tier: As of November 2026, there’s no dedicated enterprise plan with features like SSO, centralized admin controls, or usage analytics dashboards. Perplexity has indicated one is planned.
• Compliance documentation gaps: Regulated industries need SOC 2 reports, HIPAA BAAs, and data processing addenda before production deployment — none of which are publicly available for Teammates.

Perplexity has stated that “access and features will expand in the coming weeks,” so this section is a snapshot, not a final verdict.

The Verdict: Who Should Use Perplexity Teammates

Use Perplexity Teammates when:

• You need multi-model orchestration across different AI providers and want the platform to handle routing
• Your work involves coordinating multiple agents that need to hand off tasks to each other
• You’re already invested in the Perplexity ecosystem (Computer, Projects, Skills)
• Your use cases center on research, monitoring, and project coordination rather than high-volume transactional processing
• Slack is your team’s primary communication hub and you want agents accessible directly from channels

Use Claude Duet when:

• Connector breadth is your priority (6,000+ integrations)
• You need conservative security controls (no password visibility, card-blocking classifiers) out of the box
• Your team uses Claude Pro or Max and wants the most natural upgrade path
• Mobile access (iOS and Android) is required from day one

Use OpenAI Dots when:

• Your development workflow is centered on Codex and ChatGPT’s plugin ecosystem
• You need Slack and Teams integration with scheduled tasks and background app monitoring
• You want a single-model approach (GPT-6 Astra) with deep integration into OpenAI’s toolchain

Use Grok Bot when:

• You need team-scale bot deployment (up to ~50 bots) at a lower price point ($30-$200/month)
• Your team lives in the X/Cursor ecosystem
• The ability to record agent sessions and auto-generate reusable skills is valuable for your workflow

Consider self-hosting (Hermes Agent or OpenClaw) when:

• Data sovereignty and model choice are non-negotiable requirements
• You have the infrastructure and ops team to manage a self-hosted agent deployment
• You need more than 20 messaging platform integrations or custom communication adapters

Frequently Asked Questions

How much does Perplexity Teammates cost?

Teammates requires a Perplexity Max subscription at $200/month (or $2,000/year, approximately $167/month). The plan includes 10,000 credits per month, which are consumed by Teammate actions and Computer tasks. Standard Perplexity searches remain unlimited. There is no free tier or trial for Teammates; Free and Pro users must upgrade to Max via Settings → Account → Subscription.

Can Perplexity Teammates replace RPA bots?

Not as a direct replacement. Teammates operates at the goal and coordination level, while RPA bots excel at deterministic, high-volume process execution. The emerging architecture is hybrid: RPA handles the execution layer (invoice processing, data migration, compliance reporting), while always-on agents like Teammates manage the orchestration, exception handling, and decision-making layer above. Think of Teammates as the intelligent supervisor that monitors and coordinates your RPA estate, not a replacement for the bots themselves.

Is Perplexity Teammates secure enough for enterprise use?

Teammates provides environment isolation per agent, encrypted credential storage, human-in-the-loop approval gates, and resource scoping (e.g., repository-level GitHub access). However, as of its November 2026 launch, Perplexity has not published SOC 2 compliance reports, HIPAA BAAs, GDPR DPAs, or detailed audit logging specifications. Regulated enterprises should monitor the Perplexity Trust Center and request these documents before moving beyond pilot deployments.

What AI models does Perplexity Teammates use?

Teammates runs on Perplexity Computer’s multi-model orchestration engine, which routes work across 19+ AI models including Claude Opus 4.6 (reasoning), Gemini (research), ChatGPT 5.2 (long-context), Grok (speed), Nano Banana (images), and Veo 3.1 (video). The platform selects which model handles each subtask based on the nature of the work. Perplexity has not disclosed the remaining models in its roster.

How does Perplexity Teammates compare to Claude Duet?

Teammates differentiates on multi-model orchestration (19+ models vs. Claude-only), agent-to-agent handoff (Teammates can coordinate with each other), and its research infrastructure (seven parallel search modes). Claude Duet differentiates on connector breadth (6,000+ vs. 400+), security posture (no password visibility, card-blocking classifiers), mobile support (iOS and Android from launch), and price ($20/month entry vs. $200/month). Both offer persistent memory, proactive monitoring, and Slack integration. The right choice depends on whether you value model diversity and agent coordination (Teammates) or integration breadth and conservative security (Duet).

Key Takeaways

• Perplexity Teammates are persistent, always-on AI agents that own ongoing work — not one-off tasks. They plan, coordinate, monitor, learn, and act proactively across days and weeks.
• Built on multi-model orchestration: 19+ AI models including Claude Opus 4.6, Gemini, ChatGPT 5.2, and Grok, routed by task type — a unique architectural approach among always-on agents.
• Five-vendor market: Teammates competes with Claude Duet ($20/mo), OpenAI Dots ($100/mo), Grok Bot ($30/mo), and Meta Muse (free) — each with distinct strengths in integrations, security, pricing, and ecosystem.
• RPA teams should think hybrid: Teammates won’t replace deterministic RPA bots, but it will increasingly manage the orchestration and judgment layer above them — the coordination, exception handling, and proactive monitoring that currently requires human operators.
• Enterprise readiness is evolving: Strong isolation and encryption foundations, but missing compliance documentation (SOC 2, HIPAA, GDPR) and enterprise admin features at launch. Monitor the Trust Center before production deployment.
• Credit economics matter: At 10,000 credits/month on a $200/month plan, understanding per-action credit consumption is essential for cost forecasting at scale. Perplexity hasn’t published granular rates yet.
• Open-source alternatives exist: Hermes Agent and OpenClaw offer self-hosted always-on capabilities with full model choice for teams that need data sovereignty.

What to Read Next

This article is part of RPABOTS.WORLD’s coverage of the agentic AI ecosystem. Related reading:

• AI Agent Observability in Production: The Complete AgenticOps Guide (2026) — essential for monitoring any always-on agent deployment
• The One-Person AI Agent Stack: n8n vs Make vs Zapier vs Activepieces for Small Business Automation (2026) — alternative automation stacks at lower price points
• AI Agent Memory Architecture in 2026: Mem0 vs Letta vs Zep vs Cognee — deep dive into the memory systems that make persistent agents possible
• The $97.9 Billion AI Workflow Automation Market — the market context driving always-on agent adoption
• browser-use: The Open-Source AI Browser Agent Deep Dive — open-source browser automation as an alternative building block

References

1. Perplexity. “Teammates are agents for complex, ongoing work.” Perplexity Hub Blog, November 4, 2026. https://www.perplexity.ai/hub/blog/teammates-are-agents-for-complex-ongoing-work
2. AI Agent Store. “AI Agents News — Week of November 9, 2026.” https://aiagentstore.ai/ai-agent-news/this-week
3. AIMultiple. “Always-On Agents: Claude Duet vs Dots vs Grok Bot.” Updated November 5, 2026. https://aimultiple.com/always-on-agents
4. NewsBytesApp. “Perplexity takes on OpenAI and Anthropic with work-focused AI agents.” November 6, 2026. https://www.newsbytesapp.com/news/science/perplexity-launches-work-focused-ai-agents-called-teammates/story
5. AI Tool Ranked. “Perplexity Teammates Max Agents.” November 6, 2026. https://aitoolranked.com/news/perplexity-teammates-max-agents
6. Emergent. “Perplexity launches Teammates AI agents for work.” November 6, 2026. https://emergent.sh/news/perplexity-launches-teammates-ai-agents-work
7. eesel AI. “Perplexity Computer: A complete guide to the AI agent system in 2026.” March 3, 2026. https://www.eesel.ai/blog/perplexity-computer
8. PCWorld. “Perplexity’s new tool deploys teams of AI agents.” Ben Patterson, February 25, 2026. https://www.pcworld.com/article/3071595/perplexitys-new-tool-deploys-teams-of-ai-agents.html
9. ABI Research. “AI Claws Signal the Next Phase of Enterprise AI: Persistent, Collaborative Agents.” 2026. https://www.abiresearch.com/press/ai-claws-signal-the-next-phase-of-enterprise-ai-persistent-collaborative-agents
10. Perplexity Trust Center. https://trust.perplexity.ai

Social Media Post (X/Twitter):

Perplexity just launched Teammates — always-on AI agents that own your ongoing work, not just answer one-off questions. 19+ models, Slack-native, agent-to-agent handoff. Here’s what it means for RPA and enterprise automation teams 👇 [link] #AgenticAI #RPA #Automation

LinkedIn Post:

The always-on AI agent market just got its fifth major player in 90 days.

Perplexity launched Teammates on November 4 — persistent agents that own ongoing work across days and weeks, built on a 19-model orchestration engine.

They join Claude Duet (Anthropic), Dots (OpenAI), Grok Bot (SpaceXAI), and Muse (Meta) in what’s become the fastest-consolidating category in enterprise AI.

For RPA and automation architects, the question isn’t whether always-on agents will change your stack — it’s how fast. The emerging architecture is hybrid: RPA bots handle deterministic execution, while persistent AI agents manage the orchestration and judgment layer above.

Full breakdown — architecture, pricing, competitive comparison, and what it means for your automation estate — on RPABOTS.WORLD. [link]

#AgenticAI #RPA #Automation #EnterpriseAI #PerplexityAI

Image Suggestions:

1. Architecture diagram — Perplexity Teammates multi-model orchestration showing the 19+ models, Teammate isolation, Slack/GitHub/Gmail integration, and the relationship between Teammates and Computer
2. Competitive comparison visual — side-by-side comparison of the five always-on agent platforms (Teammates, Duet, Dots, Grok Bot, Muse) with key differentiators
3. Hybrid architecture diagram — showing how always-on AI agents sit above the RPA execution layer in an enterprise stack