This NEW AI Agent Builder Just K*lld Claude CoWork: Build Automations That Run 24/7 With Twin

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A new AI automation builder is changing what “no-code” really means

If you have ever tried to automate a manual task, you already know the pattern: you find a tool, then you need setup. APIs. Webhooks. Key management. Debugging. Glue code. And then, after all that, the automation still needs babysitting.

That is exactly what Twin aims to eliminate. Twin is an AI automation builder that lets you create agents in plain English so the agent can execute multi-step workflows across your tools. The big claim is bold: these agents can run 24/7, work off triggers like schedules and webhooks, and then self-heal and self-improve over time so the automation gets better without you maintaining it.

And in practical terms, Twin is less about “chatting with AI” and more about instructing AI to build and orchestrate actual workflows: scraping, enrichment, reporting, outreach, and multi-step pipelines.

Twin in plain English: what it is and why it feels different

Twin positions itself as an agent builder. Instead of requiring you to design the system from scratch, you describe what you want the agent to do. Twin then handles the orchestration: context gathering, tool connections, workflow steps, and the final output into places like Google Sheets or email reports.

In the demo-style examples, several themes show up repeatedly:

  • No coding required to create agents.
  • No API setup needed (a huge time saver for most businesses).
  • Agents can run on triggers like schedules, webhooks, and manual triggers.
  • No human supervision required for the routine work after setup.
  • Self-healing and self-improving behavior over time (the claim is that reliability improves and costs can decrease rather than increase).

That combination is why Twin feels like more than another “automation wrapper.” It is closer to building a small autonomous workforce.

Core concept: agents that automate workflows, not just single tasks

A lot of automation tools can do one narrow job. Twin’s pitch is different: you can create agents that automate entire workflows end-to-end.

For example, rather than telling an automation to “find listings,” you can specify the whole flow:

  • Search a source (like Zillow)
  • Filter by criteria (zip code, price range)
  • Enrich the results with additional attributes
  • Organize everything into a structured output (like a Google Sheet)
  • Send daily summaries to stakeholders

Once you think in “workflow steps,” Twin becomes much more powerful. You stop automating crumbs and start automating results.

Example 1: Real estate automation that finds homes and builds a daily Google Sheet

One of the clearest demos shows how a real estate-style agent can help with home hunting. The prompt described a workflow that does the following:

  • Find homes in Charlotte within a specific zip code
  • Limit results to a price range (example given: $1.0M to $1.2M)
  • Add matched properties into a Google Sheet
  • Enrich each listing with key attributes such as:
    • House size
    • Bedrooms and bathrooms
    • Lot size
    • Pool and waterfront details

The agent is described as going step-by-step: connecting to a Zillow scraper, pulling listings that match the filters, enriching properties, and then organizing the output into the sheet.

Even better, the automation can be scheduled. In the example, the output is sent daily to a partner so the user does not have to check manually.

Why this matters beyond real estate

Most businesses have the same workflow shape, even if the domain differs:

  • Collect data from a source
  • Filter for criteria
  • Enrich with additional context
  • Output to a system you already use (often Sheets or email)

Once Twin can do that flow, the domain is just the input and output language.

Example 2: UGC collaboration lead generation with scheduled email reports

The next example shifts from personal use to business use. The goal: create a daily automated “leaf finder” for UGC creator collaboration opportunities with travel and lifestyle brands.

The workflow includes searching for brands that match signals like:

  • Hotels and boutique stays
  • Restaurants
  • Travel brands actively seeking influencer partnerships

Then Twin builds an output that goes beyond a raw list. The agent produces a report (for example, via email) that includes:

  • Leads with structured columns
  • Application URLs
  • Personalized outreach email drafts
  • Quick actions to take next

The key benefit is compounding time savings. For influencer agencies (and many other lead-gen operations), the hardest part is not “finding leads.” It is doing research, organizing details, and writing tailored outreach at scale.

The real unlock: cross-platform orchestration

This type of automation succeeds when it can coordinate across tools. Twin’s claim is that agents can connect to the tools they need and handle multi-step tasks across platforms.

So instead of copying and pasting between research, spreadsheets, and outreach drafts, you set up the workflow once and let the agent produce a ready-to-use daily report.

Example 3: A Telegram fitness coaching bot that talks like a friend

Not all automations are “data pipelines.” Some are conversational experiences that reduce support load and increase engagement.

One showcased agent built a lifestyle coaching bot via Telegram. It provides fitness, nutrition, and wellness guidance and keeps a friendly, casual dialogue style. The demo emphasizes that the agent was configured with a communication style and a workflow that can run after setup.

Once you have that pattern, you can imagine coaching bots for many domains:

  • Language learning practice
  • Career coaching prompts
  • Diet or habit tracking
  • Customer education and onboarding

If your business has repetitive guidance or a “what do I do next?” question loop, a well-designed agent can become the first line of support.

Example 4: An intelligent Gmail autoresponder that doesn’t waste time on spam

Another practical example focuses on email. Instead of a basic auto-reply, the agent is described as intelligently handling unread emails in inbox and spam, generating polite responses (three to five sentences), and logging the process.

But the standout detail is classification. The demo describes that the agent:

  • Can classify emails into categories
  • Can determine whether an email looks like a real customer support request vs a promotion or notification
  • Can avoid replying when it would waste effort

After processing a sample of recent emails, it reportedly found no meaningful customer support requests in that set. The point is not that it never replies. The point is that it can decide when replying is appropriate.

Example 5: Multi-agent systems for real estate wholesale pipelines

Then Twin goes deeper into one of the most interesting capabilities mentioned: building a multi-agent system.

Instead of one agent doing everything, a multi-agent setup breaks a pipeline into specialized agents that hand off work to each other:

  • Agent 1: Market intake (source listings and add them to a Google sheet)
  • Agent 2: Distress filter (score leads for distress and move them to the next stage)
  • Agent 3: ARV estimation (estimate after repair value)
  • Agent 4: Lead qualification / verification (verify leads and prepare them)
  • Agent 5: Skip tracing (find contact information)
  • Agent 6: Lead outreach (reach out with tailored email)

Even if real estate wholesale is not your thing, this example is valuable because it shows how you can model complex processes:

  • Intake and data gathering
  • Scoring and filtering
  • Enrichment and estimation
  • Qualification and verification
  • Contact retrieval
  • Outreach and tracking

The demo also highlights orchestration: the system can build agents one by one and then run them together. When there are many leads, it can batch processing steps rather than treating everything as one-off work.

Discover mode: clone ready-to-use agents from the Twin community

One of the best ways to get value fast is to start from existing agent builds.

Twin provides a discover section where community members share agents. The workflow for exploring is straightforward: search by keyword (like scrape) and filter for relevant agents. From there, you can clone an agent and customize it for your workspace.

The example given includes an agent that creates AI avatar product videos from Amazon listings, showing that agent ideas range from scraping and data collection to content generation.

Community agent templates are also useful because they reveal how others structure workflows. Even if you never clone directly, you can copy the approach and write your own instructions.

How to start using Twin (without getting overwhelmed)

If you want to replicate the “from zero to running automation” experience, here is a simple process that works well with an English-first agent builder.

Step 1: Pick one painful manual workflow

Choose something you do repeatedly that has clear inputs and outputs. Good first targets:

  • Lead lists you build every day
  • Reports you compile weekly
  • Email triage and drafting
  • Content research and enrichment
  • Spreadsheets you update manually

Step 2: Write the workflow like steps

Before you build, describe the workflow steps clearly in plain English. Include the destination (Google Sheet, email report, Slack message), the filters, and the fields you want.

A helpful mental template:

  • Trigger: when should it run?
  • Source: where does the data come from?
  • Filter: what criteria must match?
  • Enrich: what extra fields do you need?
  • Output: where should results go?

Step 3: Start with one agent, then split into a multi-agent pipeline

Most people try to build “the perfect system” immediately. A better path is:

  • Build a single agent that completes the first version.
  • Validate the output quality.
  • Then add a second agent if you need scoring, verification, or deeper enrichment.

This is how the multi-agent real estate pipeline example illustrates scaling: each stage becomes its own specialized agent.

Where AI automation builders like Twin are headed

Twin’s positioning reflects a broader shift. The future of automation is not “rules and macros.” It is orchestrated AI agents that can handle real workflows: searching, classifying, enriching, formatting, and initiating next actions.

The most compelling parts of the approach described here are:

  • Ease of setup (no APIs or coding in the demo flow)
  • Continuous operation (schedules and webhooks)
  • Reduced supervision once the agent is built
  • Improvement over time through self-healing and self-improving behavior

And if those capabilities hold up in production for your use case, the payoff is huge: automation that does not fall apart after the first week.

External resources to explore

If you want to understand the ecosystem around automation and AI agents, these are good starting points:

FAQ

Does Twin require coding or API setup?

Twin is designed to work with plain English instructions. In the examples shown, agents were set up without coding and without requiring the user to manually set up APIs. The platform handles connections as part of the agent build process.

Can Twin agents run automatically without supervision?

The described agent behavior emphasizes that once you set them up, they can run based on schedules and triggers like webhooks, with no human supervision for ongoing tasks.

What kinds of outputs can Twin agents produce?

Common outputs include structured rows in Google Sheets and email reports. The demo examples also include drafting personalized outreach messages and building multi-step pipelines across tools.

Is Twin only for marketing and lead generation?

No. The examples span personal real estate research, influencer lead discovery, conversational coaching bots via Telegram, intelligent Gmail autoresponding, and multi-agent real estate wholesale pipelines.

How do I get ideas for my first agent?

Use the Twin community “discover” section to browse existing agents. Search by keywords like “scrape,” then clone and adapt an agent template to your workspace and goals.

Conclusion: build one automation that saves hours this week

Twin’s biggest promise is not just smarter AI. It is automation that feels practical: agents you can build in plain English, that run continuously, that connect across tools, and that produce structured outputs you can act on.

If you want the quickest win, pick one workflow you hate doing manually. Create a single-agent version first. Then expand into multi-agent stages once you trust the quality of the results.

CTA: If you found this useful, leave a comment with the one process you want to automate (leads, email triage, reporting, research, outreach). Or explore community agent builds and adapt one template to your own workflow.

Suggested multimedia additions (for publishing)

  • Image idea: Screenshot of a Twin agent workflow with steps labeled (scrape, filter, enrich, Google Sheet). Alt text: “Twin agent workflow steps for scraping, filtering, enriching, and writing results to Google Sheets.”
  • Infographic idea: “Single-agent vs multi-agent pipeline” diagram using the real estate wholesale example as a template. Alt text: “Diagram comparing a single-agent workflow to a multi-agent pipeline with staged handoffs.”
  • Video idea: Short embedded walkthrough showing how to write a plain-English agent prompt. Alt text: “Walkthrough of creating a Twin agent using plain English instructions.”

Internal links to add on your site:

External link reminder: For general background on automation concepts, see IBM’s automation overview.

This article was created from the video This NEW AI Agent Builder Just K*lled Claude CoWork (crazy use cases) with the help of AI.

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