Persistent AI Agents: How to Get Started (and What to Watch Out For)

Wondering how persistent AI agents differ from ChatGPT, Claude, and other AI chat tools? Want an always-on AI agent that manages your email, calendar, and tasks without putting your business data at risk?

​In this article, you’ll discover how to onboard, secure, and train a persistent AI agent, how to choose among Dots, Grok Bot, Muse, and Instinct, and which workflows to set up first.

What Are Persistent AI Agents and How Do They Differ From AI Chat Tools

A persistent AI agent is an always-on AI assistant that remembers everything it’s told, keeps working in the cloud whether or not its user’s computer is on, and communicates through everyday channels like text messaging. Jonathan Mast sees these agents as the next step beyond AI chat. They take on repetitive work, freeing up time for important tasks that otherwise never get done.

​Three capabilities separate them from chat tools.

​Persistent Memory: Chat tools start each conversation over, so files get re-uploaded and context gets re-explained. A persistent agent remembers what it’s been told. Jonathan compares it to an employee with a photographic memory: a process explained once never needs a reminder. When he asks what he told it the week before, it can answer that it was Tuesday and repeat the instruction.

​One Continuous Conversation: Instead of separate chats, a persistent agent runs as a single ongoing thread. Many work through text messaging, WhatsApp, Slack, Discord, or Telegram rather than an app. Jonathan’s Instinct agent is a contact in his phone that he texts like a colleague.

​Always-On Operation: The agent continues running through cloud-based services after the computer is closed. Jonathan can assign a task, shut his laptop, and continue the conversation from his phone or tablet at a meeting, at dinner, or on vacation.

​Setup is also far simpler than it used to be. For most of 2026, tools like OpenClaw and Hermes required a virtual private server or a dedicated computer, and Jonathan once spent 12 hours on the phone with his IT person to get OpenClaw running. Newer agents ask for access when they need it, such as “I need access to your Google Calendar,” and an authorization screen appears to approve or decline. No terminal commands are required.

​Jonathan cautions against the biggest misconception he sees: that an agent can run the business while the owner sits on a beach. That’s nowhere near reality. AI amplifies a person’s skill and experience, and the person is still needed in the process.

#1: How to Choose a Persistent AI Agent: Dots, Grok Bot, Muse, and Instinct

Jonathan recommends choosing one persistent AI agent and committing to it. The four leading options are fundamentally similar, and the main source of value, persistent memory, builds only when one agent knows what the user is working on over time. That’s why Jonathan, who has access to all four, primarily uses Dots.

• Grok Bot, from X, was one of the first and, in Jonathan’s view, started the easy-setup category.
  
  
• Muse, from Meta, is currently free, and Meta is giving away billions of free tokens.
  
  
• Dots comes from ChatGPT.
  
  
• Instinct is a newer entrant that’s popular with Wall Street and is currently free.

To Test Without Spending: Jonathan suggests using Muse or Instinct while they’re free, noting that AI tool pricing changes quickly. Free tools come with a tradeoff: users need to be comfortable with which company gets access to their data.

​For Teams Already Using ChatGPT: Jonathan’s leadership team chose Dots because the company is already familiar with the ChatGPT interface. At the time of recording, Dots requires one of ChatGPT’s team or enterprise-level business plans at $125 per user per month, which also includes 5 to 10 times more usage. Jonathan says an agent will consume that extra usage.

​For Grok Bot Users: Jonathan rates Grok Bot highly, and he and his CEO both still have accounts, but he doubts he’ll keep paying for it past November because the team won’t use it enough to justify paying for two agents.

​To judge the cost, Jonathan compares it with the time saved. His team’s agents add $600 a month, and some people on Facebook called that crazy. But these are his A players, and if the subscription makes them even 5% more efficient, it pays for itself almost immediately. He estimates the gains have been closer to 20%-30% in the first two weeks, while the team is still learning.

#2: How to Onboard a Persistent AI Agent Like a New Employee

Before granting an agent any access, Jonathan recommends making the same three decisions a business makes before a new hire’s first day: what the agent can do without approval, where the agent’s work goes, and when the agent can use the owner’s computer. He calls this agent onboarding.

How to Set Privacy and Security Rules

Jonathan frames each rule as a yes-or-no permission. Can the agent send an email without approval? Can it delete a file without approval? His baseline rules are:

• The agent may not make any purchases without approval, even though the agent has no access to his credit cards.
  
  
• The agent may not delete files from his computer or any online service without approval.
  
  
• The agent must check before sharing files.
  
  
• The agent may not send messages to anyone, including his team, or commit him to anything without approval.

Access should match trust. A new employee doesn’t get the keys to the safe on day one, even though a CFO who has been with the company for years might. Jonathan treats agents the same way.

​Jonathan also recommends choosing an agent that assumes it needs permission rather than one that assumes it can do anything until told to stop. That second behavior was one of the big concerns when OpenClaw launched; Jonathan compares it to a capable child who assumes anything they weren’t told not to do is allowed. The newer agents all default to asking for approval.

​For anyone unsure where to draw the lines, the agent can walk through the decisions and answer questions. Jonathan has also created a set of recommended security and privacy baselines as a starting point.

​Prepare Freely, Ask Before Acting: Jonathan explicitly gives his agents permission to research, prepare, and think ahead without approval, while requiring approval before anything goes out to another person or commits him to something. Under this rule, the agent can plan his day, build a bio on someone before a call, research them, and review notes from past conversations without asking.

​The rule lets the agent anticipate needs. After a hurricane damaged Jonathan’s roof and he mentioned it to his assistant, the agent researched the top five roofers in his area that do repairs and asked whether to reach out or request quotes. It stopped there and contacted no one.

How to Define Where the Agent Works

An agent needs a defined place to deliver work. Jonathan’s company uses Google Drive, so he grants each agent access to specific shared folders rather than the entire Drive, the same access a new employee would get. Then he tells the agent where to save its work, how to format it, and why.

How to Keep the Agent Off the Local Computer

Most persistent agents can work on a cloud-based virtual computer and can also take control of the user’s own computer. Jonathan compares local access to an assistant allowed to use the keyboard and mouse: helpful when the owner is out of the office, disruptive when the assistant switches windows mid-sentence and sends typing to the wrong place.

​Jonathan instructs his agents to use their virtual computer whenever possible, especially for email and calendar work, and defines when they may and may not take control of his machine. The cloud browser is faster, keeps the agent from taking over his screen, and lets it work whether his computer is on or not.

#3: How to Train a Persistent AI Agent on the Business and Its People

A new agent needs the same training as a new employee: who the company is, who it serves, and what products and services it offers. Because Jonathan expects nearly everyone will also use their agent for personal tasks, he recommends teaching it about each user as well.

How to Transfer What Existing AI Tools Already Know

The fastest way to train an agent on its user is to have the AI tools that already know how that person writes the training material.

​Request a Self-Profile: Jonathan asked both ChatGPT and Claude to create a comprehensive document containing everything they knew about him that a new agent should be trained on. One returned a 30- to 40-page document; the other returned about 50 pages.

​Include the Team: His documents covered his family, every staff member and their role, and their email addresses and phone numbers so the agent could reach them through WhatsApp if needed.

​Upload With a Clear Instruction: Jonathan gave both documents to his new agent and told it to learn the information so it could serve him more fully, and not to forget it.

​The training pays off immediately. When a member emailed Jonathan directly about a coupon code that wasn’t working, the agent flagged it as a support issue and asked whether to send it to Sarah, his head of customer support. No one had told it who handles support. Once Jonathan approved, the issue was routed, and the agent reported about 30 minutes later that it had been resolved.

How to Use Personality Tests to Tailor Communication

Personality test results tell an agent how each person thinks and communicates, so it can deliver the same message in the form each recipient understands best. Because frameworks like DISC and Myers-Briggs are well-documented, the AI already knows what each result means.

​Assess the Team: Jonathan’s company runs a DISC assessment on every new employee and also uses Myers-Briggs. Jonathan is a high D with some I on DISC and believes he’s an ENTP.

​Build a Shared Reference Sheet: The HR department maintains a Google Sheet listing every employee’s email address, position, Myers-Briggs type, and DISC profile. Jonathan’s agent has access to it.

​Instruct the Agent to Adapt: The agent is told to stay true to who the company is while writing each message in the way the recipient will best understand. A memo from Jonathan to his detail-oriented HR director or director of operations still sounds like him, but is framed for how they process information. Jonathan calls it the same message wrapped in different paper, and he says it keeps AI output from reading as slop.

​Include the User’s Own Preferences: The user’s own profile shapes how the agent reports back. Jonathan’s agent gives him bullet points and the big picture, never a 17-page step-by-step plan. His wife, who works in operations, gets paragraphs, details, and offers to expand into step-by-step instructions.

​The same understanding helps with relationship habits that don’t come naturally. Jonathan is direct and skips small talk, but knowing which team members value being asked about their weekend lets him take the extra 10 seconds that build rapport.

#4: Start a Persistent AI Agent With Email and Calendar Access

The first workflow Jonathan recommends, especially for anyone testing an agent for a month, is email and calendar management.

​Connect Email and Calendar: The agent gets access to both accounts before anything else.

​Set Two Rules First: No scheduling appointments without permission, and no sending email without permission.

​Let It Triage: The agent sorts incoming email, identifies what needs a response, and learns from how Jonathan has handled messages in the past. It flags what he needs to deal with and assigns other items to team members.

​Have It Clean Up the Backlog: Jonathan’s agent reviewed his older messages, organized them, and surfaced items he’d missed.

​Jonathan used to spend hours on email some days, often at night after a full day of meetings. Within a few days, he was down to minutes a day. He calls it probably the single biggest productivity gain he’s gotten from his agent.

#5: Five More Workflows to Set Up With a Persistent AI Agent

Once an agent is onboarded and trained, Jonathan uses it to run recurring workflows that used to take hours or never got done. Each starts with a single request and runs inside the approval rules already in place.

​Hand Off Work Between Team Agents: Jonathan has rolled out persistent agents to six or seven people, including his chief of staff, CEO, head of customer service, and operations director. The agents share workspaces, and where they can’t talk to each other natively, the team uses tools that let them communicate. Jonathan taught his agent the approval workflow for work that his CEO reviews before it goes out. After he approves a piece, his agent sends it to Dottie, CEO Beth’s agent, which tells Beth it’s ready. The agents can also collaborate on shared files and projects while the humans see and join the work.

​Build a Shared Reporting Dashboard: The setup starts with describing what the dashboard needs to show and who will use it, then asking the agent what it needs to build it. Jonathan made that request for a dashboard to share with Sarah and Beth during a membership migration that left data split across PayPal, Stripe, and a new management system. The agent mocked up a version first. Now the team’s agents are told what data is needed, pull it each morning, and update a dashboard anyone can check at any point in the day. No developer built or maintains it, and it saves hours of meetings.​

Automate Subscription Renewal Reviews: With email access, the agent can send a morning message listing renewals received overnight. For any subscription Jonathan doesn’t want, the agent finds the cancellation path, including buttons buried deep in a website, and submits the cancellation with his approval. His agent proposed this workflow on its own after analyzing his email.

​Run a Daily Task Review: Rather than prescribing a system, Jonathan told his agent the problem: he has ADHD and needed help being organized. The agent noticed that he had added tasks to Google Tasks daily but hadn’t completed one in months, and proposed a daily review. They now spend 10 to 15 minutes each day marking what’s done. When he tried skipping it, the agent reminded him the next morning. It also breaks his larger business goals into the small daily steps needed to reach them.

​Track Commitments and Catch Conflicts: Connecting the agent to the calendar, voice recordings, and meeting transcripts lets it track tasks, the big rocks to focus on, and commitments made in conversation. It reminds Jonathan of promises he forgot. During a presentation, he suggested meeting the following Thursday, and about 10 seconds later, the agent alerted him on screen that he’d be out of town. He believes it combined his calendar with his live transcript.

Other Notes From This Episode

• Connect with Michael Stelzner @Stelzner on Facebook and @Mike_Stelzner on X.
• Watch this interview and other exclusive content from Social Media Examiner on YouTube.

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