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7 MIND BLOWING Automations You Won’t Believe You Can Do With AI Agents (New Use Cases)

Sofia Alvarez

Table of Contents

🔎 1) Automated Code and PR Review — Have an AI Audit Your Repos

One of the most jaw-dropping things I showed is getting an AI agent to analyze pull requests in a GitHub repo and leave detailed review comments — automatically. Think about it: a tool that reads your code, flags security holes, lists maintainability problems, and returns an ordered list of fixes with priorities. That’s exactly what I built with DeepAgent in my demo.

Why this matters: code review is a bottleneck for many teams. Human reviewers miss things, get overloaded, or introduce delays. An AI agent can consistently apply a checklist to every PR, surface issues early, and save engineering time for the problems that truly need human judgment.

How it works in practice:

Concrete prompt you can reuse:

“Review open pull requests in this repository. Provide: (1) critical security vulnerabilities, (2) maintainability issues, (3) performance hotspots, and (4) exact code changes (diffs) recommended. Prioritize by severity and include remediation steps.”

When to use this:

📝 2) Weekly Blog Automation — Publish to WordPress Without Lifting a Finger

Content is a time sink. Writing, formatting, and publishing weekly blog posts can eat hours from your schedule. I showed how an AI agent can be tasked to write a weekly post, format it to your style, and either draft it for approval or publish it directly to your WordPress site.

What the agent does:

  1. Asks clarifying questions: target audience, tone, length, topics to prioritize, and any custom style guide.
  2. Schedules posts on a defined cadence (weekly, monthly, etc.).
  3. Generates the content, formats it for your WordPress editor, and publishes to a specific URL or drafts for human approval.
  4. Notifies you via email or Slack when posts are published (you can customize notifications).

Sample automation workflow I used:

Why this is valuable:

This drastically cuts the content production cycle. You keep editorial control (approve or edit drafts), or you give the agent permission to publish. Either way, you spend far less time on ideation, drafting, and formatting.

🎨 3) Refactor and Re-theme Websites — AI as Your Front-End Dev

Imagine telling an AI, “Refactor this website’s UI and style it to a Netflix theme with a dark mode toggle,” and the system creates a new branch, updates the styles, and pushes the changes to your repository. That’s what we did in the demo.

This goes beyond small CSS tweaks. The agent can:

How to frame the prompt:

“Connect to my GitHub repo. Create a new branch called ‘refactor-netflix-theme’. Refactor the UI to adopt a Netflix-like aesthetic: dark background, large hero banner, carousel cards, hover animations. Add a theme switcher to toggle between original and Netflix themes. Run any build/tests and push changes.”

Practical uses:

📣 4) 24/7 Lead Generation Agent — Find, Qualify, and Outreach Automatically

One of the most compelling business use cases is automating lead generation and outreach. I showed an agent that finds 25 qualified local business leads in California (cafes, clinics, real estate), analyzes their sites for quick wins, drafts personalized outreach, and can either fill contact forms or email them directly.

What this AI agent offers:

Example prompt pattern:

“Create an AI agent that helps my web design agency generate qualified leads from California. Objective: find 25 local businesses from cafes, clinics, and real estate. For each, provide a short site audit (1–2 bullet points), craft a personalized outreach email, and record results to a Google Sheet.”

Business impact:

This turns lead gen into a predictable, scalable process. Instead of ad-hoc prospecting, you have 24/7 outreach that learns from responses, maintains follow-up, and fills your pipeline while you sleep.

🛠️ 5) Clone & Rebrand Open Source Projects — Customize Without Coding

The AI agent can take an open-source project, clone it, and apply brand changes and feature tweaks based on a simple plain-English instruction. In the demo we cloned an open-source calculator game and rebranded it with a new color palette, updated titles, and pushed the rebranded version to a new branch on GitHub.

Why this is powerful:

Example workflow:

  1. Provide a repo URL for the open-source project.
  2. Tell the agent desired brand specs: color palette, fonts, logo, new names.
  3. Agent clones, modifies files, runs a build, and pushes to a new branch.

Prompt you can reuse:

“Clone this repo. Apply Abacus AI color palette, update the game title to ‘Abacus Theme’, replace primary colors and logos, and push to branch ‘abacus-theme’. Run build and upload the preview link.”

📊 6) Automated Reporting — Salesforce, Ads, Shopify, and Beyond

Stop manually pulling reports. An AI agent can log into dashboards (Salesforce, Google Ads, Shopify), gather data, generate structured summaries, and deliver human-readable reports or dashboards. In my example I created a Salesforce data summarization agent that produced weekly performance summaries per rep using markdown tables.

What this agent does:

Sample instruction:

“You are a Salesforce data summarization agent. Analyze deals closed and pipeline activity, and generate a weekly performance table per rep. Include total activity, conversion rate, top recommendations, and a short executive summary using markdown tables.”

Real-world value:

Marketing teams, sales ops, and agency owners can replace manual spreadsheet wrangling with automated analysis that delivers actionable insights every week. The agent can also highlight anomalies — like sudden drops in conversion — enabling faster interventions.

🔒 7) Security & Code Quality Checks for AI-Generated Apps

Many people are using AI-assisted coding tools (vite, Copilot, etc.) to build apps quickly. But speed often introduces security oversights. I demonstrated having an agent audit a repo built with AI assistance and it highlighted critical issues like exposed admin credentials, client-side authentication problems, insecure storage, markdown injection risks, and unsafe file uploads.

Capabilities of the agent:

Prompt example:

“Review the repository and provide: (1) potential security vulnerabilities, (2) implementation techniques used, (3) suggestions for improving structure, performance, and maintainability. Highlight critical issues and provide recommended fixes.”

Why this is critical:

If you don’t know code and you’re generating apps with AI, you need a safety net. These agents act like an automated security auditor that knows where common AI-assist patterns go wrong and can fix or flag them before deployment.

⚙️ How to Get Started — Setup, Pricing, and Best Practices

Getting started with these automations is easier than you think. I used DeepAgent in my demos, and the setup pattern is similar across most agent platforms:

  1. Create an account (DeepAgent offers a low-cost entry; in my demo I mentioned a $10/month plan available in the pinned comment and on their site).
  2. Connect the services you want the agent to access: GitHub, WordPress, Google Drive, Gmail, Salesforce, Shopify, etc.
  3. Write a clear natural language prompt describing the objective, constraints, and desired output format. Include examples if helpful.
  4. Answer the agent’s clarification questions — this step is crucial for reliability.
  5. Test on a small scope first (one repo, a sample blog post, 5 leads), then expand automation once you verify outputs.

Best practices for reliability and safety:

🔍 Comparing Tools and When to Use DeepAgent

There are many agent platforms popping up, but a few features set DeepAgent apart based on my testing:

If you’re experimenting with AI agents for the first time, choose a platform that provides clear guidance, follow-up prompts, and sandboxed testing so you can safely test automations before applying them at scale.

✨ Advanced Tips, Prompts, and Patterns

Once you’re comfortable with basic automations, these patterns will unlock more advanced capabilities:

Prompt engineering example for multi-step tasks:

“Step 1: Find 25 cafes in San Diego and evaluate their websites for mobile-friendliness and booking flow. Step 2: For each, provide a 2-sentence audit and a personalized outreach email. Step 3: Log results in Google Sheets and schedule a 2-week follow-up if no reply.”

Small but crucial trick: always ask the agent to output a short summary at the top and a detailed appendix at the bottom. That way stakeholders can read a quick one-paragraph executive summary or dig into the full findings.

🧭 Ethics, Safety, and Permissioning

While these automations are powerful, they must be used responsibly:

Always include human-in-the-loop controls for actions with reputational or legal impact (public posts, contract changes, bulk outreach). Agents excel at repetitive tasks; humans should stay in the loop for ethical judgments.

📦 Suggested Image and Multimedia Placements

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🧾 Meta Description & Tags

Meta description (150–160 characters): Learn seven beginner-friendly AI agent automations — code review, blog publishing, lead gen, refactors, reporting, security audits, and open-source rebrands.

Suggested tags: AI agents, DeepAgent, AI automation, automated code review, lead generation automation, WordPress automation, AI for developers, reporting automation.

📌 Resources & Where to Start

To try these automations yourself:

✅ Conclusion — Why You Should Try AI Agents Today

AI agents are no longer niche toys. They’re practical tools that streamline real tasks — from code reviews and security audits to content production, lead generation, and full-site refactors. The consistent theme I showed is simplicity: tell the AI what you want in plain English, answer a few clarifying questions, and it runs a script that actually delivers a result.

I’ve reviewed hundreds of tools, and DeepAgent stands out for beginner-friendliness, the ability to spin up browser automation, and access to multiple LLMs through a single dashboard (ChatLM). Start small — run one scheduled report, draft a blog post, or let the agent audit a single PR. Once the output meets your bar, scale up.

If you want to get started quickly, I recommend trying the lead generation and the blog automation first — they provide immediate, measurable ROI and help you get comfortable with orchestration, permissions, and review gates.

❓ FAQ

How much technical skill do I need to set up these automations?

Minimal technical skill is required to start. Most agent platforms like DeepAgent have point-and-click connectors for common services (GitHub, WordPress, Gmail). You need to write clear objectives in plain English and respond to a few clarifying questions. For complex tasks (deep code refactors), basic familiarity with Git and testing is helpful so you can review outputs.

Are these automations safe to run with production credentials?

Use caution. Grant least-privilege access and start in staging/test environments. For production credentials, use a secure secrets manager and restrict the agent’s access to only the repositories or sites it needs. Always include manual approval gates for destructive or high-impact actions.

Can AI agents actually push code changes that work?

Yes — many simple to intermediate changes (styling, refactors, dependency updates, tests) can be successfully automated. Complex architectural changes or nuanced business logic should be reviewed by engineers. In my demos, the agent created branches, ran builds, and pushed changes ready for human review.

How do agents handle follow-ups and interaction with humans?

Agents can be scheduled to re-run tasks, check for responses (e.g., leads replying), and trigger follow-ups. They can also send notifications via Slack or email and create task items in your workflow tools so humans can step in when needed.

What are the legal/privacy concerns with automated outreach?

Automated outreach must comply with local spam and privacy laws (e.g., CAN-SPAM in the US, CASL in Canada, GDPR in the EU). Don’t scrape personal data in ways that violate terms of service or privacy regulations. Prefer consent-based approaches and consult legal counsel for large-scale campaigns.

Where can I learn more and see examples?

Try building small experiments: set up an agent to draft a blog post or run an audit on a sample GitHub repo. Document the prompts and results so you can iterate. If you’re interested in structured learning, look for courses or communities focused on AI automation and prompt engineering.

📣 Final Call to Action

If you want to build any of these automations and need starter prompts or example workflows for your specific use case (agency, ecommerce, SaaS, or dev team), leave a comment below or reach out via the contact details in my channel’s description. Start simple, validate outputs, and then scale — automation like this changes how we work, and the most successful teams will be the ones that adopt it thoughtfully.

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