Table of Contents
- 🤖 What is DeepAgent and why this upgrade matters
- ⚙️ How the “spin up a computer” feature works — a technical and practical overview
- 🏡 Use Case #1 — Zillow search and real-estate market analysis
- 💼 Use Case #2 — Job hunting on LinkedIn
- 📊 Use Case #3 — 11 Labs credit usage reporting (automated analytics)
- 🔗 Use Case #4 — LinkedIn outreach at scale (CEOs, decision-makers)
- 🎶 Use Case #5 — Curated YouTube playlists and content aggregation
- 🧭 A step-by-step demo walkthrough (how you’d actually use it)
- 🔒 Security, privacy, and best practices
- 💡 Practical tips for beginners
- 💸 Pricing and getting started
- ⚖️ Ethical and legal considerations
- 🚀 Advanced possibilities and workflow ideas
- ❓ FAQ — Common questions about using DeepAgent
- ✅ Conclusion and next steps
🤖 What is DeepAgent and why this upgrade matters
DeepAgent is an AI automation platform that lets you create “agents” — autonomous AI workers that can take instructions in plain English and then act inside a browser or other integrated environments to accomplish tasks. The recent upgrade I’m excited about extends that capability by allowing DeepAgent to spin up its own browser session (effectively its own “computer”), log into web apps, interact with UIs, execute JavaScript in a console, and return structured results to you.
Why this matters: most AI tools can help you generate text, summarize content, or suggest actions. DeepAgent can now execute those actions in real environments — websites like Zillow, LinkedIn, YouTube, SaaS dashboards, and more. That turns AI from an assistant that tells you what to do into a worker that does it for you.
⚙️ How the “spin up a computer” feature works — a technical and practical overview
At a high level, here’s what happens when you give DeepAgent a task:
- You describe the task in plain English. For example: “Do a Zillow search for condos in Charlotte with X/Y/Z criteria.”
- DeepAgent asks clarifying questions — for location specifics, login choices, or preferences.
- It creates a fresh browser session (its own computer) and navigates to the target site.
- It can log in using credentials you provide (and it has memory to store and reuse them if you allow).
- It interacts with the UI like a human would: clicking buttons, applying filters, scraping results, running scripts in the browser console, downloading or formatting data, then producing a report or performing next actions (like sending an email, creating an Excel file, or messaging someone).
This isn’t just “web scraping.” DeepAgent can perform complex multi-step flows: find things, analyze them, compare them, format results, and take follow-up actions. It can also schedule recurring tasks (daily, twice a day, etc.) so the automation runs on your timetable.
🏡 Use Case #1 — Zillow search and real-estate market analysis
One of the first demos I ran was a real-life example: I asked DeepAgent to search Zillow for modern condos in Charlotte, NC with specific constraints. The agent launched a browser session, applied filters, reviewed listings, and returned a formatted market analysis summary plus recommended properties. Here’s the prompt I used as an example:
For example, I’m considering relocating to Charlotte NC and I’d like you to do a Zillow search for me. I want a modern condo with at least two bedrooms, two bathrooms, and an open floor plan by price range is 800,000 to 1.2 million and it should be at least 2,000 square feet with access to public transfer nearby.
After initial clarifying questions (e.g., specific neighborhoods? distance to transit? desired amenities?), DeepAgent spun up a browser and executed the search, then produced:
- A market analysis summary with key findings
- Best areas/neighborhoods for the criteria
- Recommended properties and buildings
- A tally of how many properties were reviewed and timestamps
- Next steps (e.g., contact building, schedule viewing)
What made this impressive was not just the scraping but the formatting: it created an actionable report and gave suggestions like “follow up with specific buildings” and offered to contact them on your behalf if you wanted. This could transform house-hunting or property research workflows for buyers, renters, and real-estate pros.
💼 Use Case #2 — Job hunting on LinkedIn
Next I used DeepAgent to automate a targeted job search on LinkedIn. Rather than scrolling through postings manually, I instructed the agent to log into my LinkedIn, scan the Jobs section, and build a table of opportunities that matched my profile. The instruction looked like this:
Log into my LinkedIn account and go through the job section on my LinkedIn profile and make a list of at least 10 jobs posted in the last one week which are a good fit for my profile based on my profile and my job description that was posted. Output a table with company name, role, link to hiring manager, location, link to opening, and why it’s a good fit.
What DeepAgent delivered:
- Login to LinkedIn (after you confirm credentials)
- Filtering by posting date and matching against profile
- Extraction of hiring manager information where available
- A neat table of at least 10 job matches with reasons why each is a fit
This essentially replaces the routine job-scouting done by recruiters or active job seekers. You can take the output and use it to apply, reach out to hiring managers, or have the agent tailor a message and send connection requests or follow-ups automatically.
📊 Use Case #3 — 11 Labs credit usage reporting (automated analytics)
Automation isn’t only for searching or outreach. I demonstrated how DeepAgent can log into a SaaS product like 11 Labs, navigate to workspace analytics, extract credit usage for the last 24 hours, format that into an Excel file, and email it to stakeholders.
The agent asked clarifying questions (which workspace? which email? schedule frequency?), then:
- Logged into 11 Labs
- Selected the right tab (workspace vs account)
- Scraped the usage metrics
- Formatted an Excel and a nicely formatted email message
- Sent the results to the specified recipient
You can have this run every day and receive a ready-to-read report without lifting a finger. For teams that monitor API or token usage, cost, or credits — this type of automation is gold.
🔗 Use Case #4 — LinkedIn outreach at scale (CEOs, decision-makers)
For lead generation and networking, I had DeepAgent search LinkedIn for CEOs of seed/Series-A tech companies and send connection requests. The agent can:
- Search using specific criteria (industry, title, company size)
- Send personalized connection requests or follow-up messages
- Report back with campaign metrics (connected, pending, failed, profile issues)
Example results showed: “Processed 12 out of 30 CEOs, already connected with nine, three profile issues.” You can use this as an automated outreach channel while keeping the “personalization” tone consistent across messages.
🎶 Use Case #5 — Curated YouTube playlists and content aggregation
As a creator, I love having tools that help me research and curate. I asked DeepAgent to search YouTube for the most popular jazz tracks globally and create a private playlist called “Jazz Evergreen” with at least 20 tracks. It:
- Found popular tracks across channels
- Created a private playlist
- Added chosen videos and returned a link to the playlist
Beyond music, the same pattern works for building resource lists, podcast playlists, curated learning paths, or competitive content audits across any web platform.
🧭 A step-by-step demo walkthrough (how you’d actually use it)
Here’s a practical walkthrough you can follow to create an agent for a common task like the Zillow search. This is the flow I used and recommend for beginners:
- Open DeepAgent and click “Create agent” or “New task.”
- Type a concise instruction in plain English describing the objective:
“Search Zillow for modern condos in Charlotte, NC. Criteria: at least 2 beds, 2 baths, open floor plan, 2,000+ sqft, price $800k–$1.2M, within 0.25 miles of public transportation. Prefer gym and pool. I’m looking to buy within 1–2 months. Produce a market analysis and a list of recommended properties (with links).”
- Answer clarifying questions from the agent (neighborhood specificity, distance to transit, preferred amenities).
- Confirm login if necessary (you can hand over credentials; DeepAgent can store these securely if you allow memory).
- Let the agent run. Watch the browser activity or let it run in the background.
- Receive a formatted report: summary, property list, and next steps.
That’s it. You can then ask the same agent to take follow-ups: “Contact the leasing office for these three properties and ask about HOA fees and storage availability,” and it will do it, given the right permissions.
🔒 Security, privacy, and best practices
With great power comes responsibility. When you allow an AI agent to log into services and act on your behalf, consider these best practices:
- Use dedicated service accounts or app-specific passwords: For critical accounts, prefer app-specific or limited-permission tokens.
- Limit memory and access: Only permit DeepAgent to store credentials if you trust the platform and understand how to revoke access.
- Audit logs: Regularly check what actions your agent has taken. DeepAgent provides activity reports for that reason.
- Be mindful of platform TOS: Some websites restrict automated access. Use responsible scraping and follow site terms.
- Use multi-factor authentication (MFA): Where possible, use MFA and understand how the agent handles 2FA flows.
- Test in a sandbox: Try new automations in non-production accounts first to avoid unintended consequences.
💡 Practical tips for beginners
If you’re just getting started with DeepAgent, here are my top tips so you can avoid frustration and accelerate adoption:
- Start small: Build simple agents that do one thing reliably — e.g., daily scrape and email a report.
- Ask clarifying questions: The agent will often ask follow-ups. Answer them as specifically as possible to avoid ambiguous results.
- Save templates: Reuse prompts and tweak them. You’ll build a library of automations faster than you expect.
- Schedule smartly: If you set recurring tasks, stagger them so you’re not triggering heavy processes all at once.
- Use descriptive naming: Name your agents based on function and frequency: “Zillow_Charlotte_Search_Daily” is more useful than “agent1.”
- Combine with other tools: Use outputs to drive other workflows: the agent can create an Excel that triggers a Zapier workflow, for example.
💸 Pricing and getting started
DeepAgent has options to get started affordably. In my video I mentioned a recommendable entry option at around $10/month (check the pinned comment in the video for the referral link). When you sign up you typically get:
- Access to the agent builder and running browser automations
- Memory to store credentials and preferences (optional)
- Access to chat LLM that lets you query multiple language models from one place
Direct link suggestions: DeepAgent signup via https://deepagent.abacus.ai/rqm and AI Automation School info at https://www.skool.com/ai-automation-school/about
⚖️ Ethical and legal considerations
Automating actions on behalf of users raises ethical and legal issues you must consider:
- Consent: Only automate accounts for which you have explicit permission.
- Transparency: When reaching out to people (e.g., LinkedIn outreach), be honest and avoid deceptive messaging.
- Data protection: Ensure sensitive data (PII, financial info) is stored and transmitted securely.
- Compliance: Some industries have strict automation and communication rules (e.g., healthcare, finance). Know the rules before acting.
Responsible automation preserves trust and avoids operational or legal risk.
🚀 Advanced possibilities and workflow ideas
Once you’re comfortable with basic automations, here are advanced workflows you can build with DeepAgent:
- Multi-agent funnels: Chain agents together — one finds opportunities, another personalizes messages, another follows up and schedules meetings.
- Data enrichment pipelines: Scrape listings, cross-reference with other sources, enrich with demographic or financial data, and export to BI tools.
- Creator workflows: Aggregate competitor content, compile resources into playlists or reading lists, and auto-generate summaries for video scripts.
- Customer success automations: Monitor usage dashboards, flag anomalies, open tickets, and notify stakeholders automatically.
- Productivity stacks: Integrate DeepAgent with Zapier/Make, Slack, Google Workspace, and CRMs to auto-create leads, tasks, and calendar events.
❓ FAQ — Common questions about using DeepAgent
How does DeepAgent log into my accounts securely?
DeepAgent can store credentials in encrypted memory if you allow it. Use app-specific passwords or tokens where possible. Treat stored credentials like any password manager entry — restrict access and know how to revoke permissions.
Can DeepAgent handle two-factor authentication?
It depends on the MFA method. If your MFA requires manual confirmation via a device you control, you’ll often need to provide the code during setup or use app-specific tokens. Always test in a controlled environment first.
Is automation detectable by websites (and could it violate terms)?
Some sites monitor for automated activity. Use responsible throttle rates, mimic human interaction patterns when needed, and respect robots.txt and terms of service. If you’re unsure, consult the website’s policy or use APIs when available.
What happens if the website UI changes?
Since DeepAgent interacts with UI elements, significant changes to a website may break your automation. Build robust selectors (or rely on visible text/semantic anchors), and monitor tasks for failures to catch issues early.
Can DeepAgent send emails and format attachments?
Yes — DeepAgent can compose emails, create and format Excel files, and attach them to messages. In my demo it formatted 11 Labs usage into an Excel and crafted an email message automatically.
How do I schedule recurring automations?
When creating a task you can specify recurrence (daily, twice a day, etc.). DeepAgent will run the agent on the schedule and optionally send you or other recipients reports.
What are good starter automations for non-technical users?
Start with simple daily reports: usage metrics from a SaaS dashboard, a digest of new job postings that match your profile, or a weekly list of competitor blog posts. These are high ROI and low risk.
Where can I learn to build more advanced workflows?
I recommend learning resources like AI Automation School (https://www.skool.com/ai-automation-school/about) and DeepAgent’s documentation. Practice by building progressively more complex agents and joining communities to exchange prompts and strategies.
✅ Conclusion and next steps
DeepAgent’s new capability to spin up a browser “computer” and execute real-world tasks is a watershed moment for personal and team automation. Whether you want to automate property searches, job hunting, SaaS reporting, outreach, or content curation, this tool is now capable of doing the heavy lifting. I encourage you to start small, keep security front of mind, and iterate — you’ll be surprised how quickly you can replace repetitive manual work with reliable autonomous agents.
Want to get started right now? DeepAgent signup: https://deepagent.abacus.ai/rqm. If you’re serious about learning automation end-to-end, check out AI Automation School: https://www.skool.com/ai-automation-school/about
Meta description suggestion (150–160 characters): Learn how DeepAgent’s new browser-based AI agents let you automate tasks like Zillow searches, LinkedIn job hunts, analytics reports, and outreach in minutes.
Tags and categories to use: DeepAgent, AI automation, browser automation, workflow automation, job search automation, LinkedIn automation, SaaS reporting, Rob The AI Guy.
Call to action: Try building one simple agent today — pick a repetitive task you do weekly, convert it into an instruction, and let DeepAgent run it for you. Share your results, ask questions, or request a walkthrough in the comments.