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One upward push turns your monitor into wall art instantly

DuoShift replaces notifications with a physical, deliberate end-of-day gesture

Compact apartments finally get a desk built for two lives

Compact living spaces have made it increasingly difficult for people to separate their professional and personal lives within the same four walls.

Seung Bin Bae, a Korean student designer, has created a dual-purpose workstation called DuoShift aimed at fixing one of remote work's most persistent problems.

DuoShift addresses this issue through a single physical motion rather than relying on software, apps, or scheduled reminders.

A single motion replaces years of software fixes

The desk operates in two distinct modes, the Work Mode and Life Mode, and switching between them only needs an upward shift of the screen.

In Work Mode, it functions as a standard productivity monitor , holding spreadsheets, browser tabs, and video calls during the day.

Pushing the screen upward shifts it into Life Mode, where it becomes a digital art frame instead.

This transformation simultaneously clears the desk surface beneath it, returning the space to non-work use entirely.

Unlike calendar apps or notification systems that attempt to impose discipline through software, DuoShift relies on a deliberate physical gesture to mark the day's end.

Bae's approach treats this transition as a ritual, similar to closing a laptop or changing out of work clothes after finishing a shift.

Visually, the design remains minimal and slim, intended to integrate into a living space rather than dominate it visually.

Why this matters beyond the desk itself

Compact urban living continues to expand rather than recede, and remote work remains common years after the pandemic reshaped daily routines.

Most monitor designs have not adapted to treat this overlap between living and working as a serious design problem.

DuoShift's customizable frame finishes also allow it to either blend into a room's interior or stand out as a deliberate design object.

Beyond its function, the product hints at commercial possibilities, including digital art subscriptions or collaborations with furniture and interior design brands.

Its modular structure additionally permits upgrades without full replacement, a detail aimed at reducing electronic waste and extending product lifespan over time.

As compelling as the concept is, it remains a student project without the manufacturing scale needed to reach a global audience.

Samsung, as one of South Korea's most powerful electronics brands, could realistically turn this idea into a mass-market product.

Without backing from a company of that size, an innovation this promising risks staying confined within South Korea's borders.

The project was named an Honoree in the Home & Living category at the Core77 Design Awards .

Whether the concept could scale into mass production through a partnership remains an open and unanswered question.

What DuoShift demonstrates, regardless of its commercial future, is that a single deliberate gesture can offer something software has consistently struggled to provide.

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You've probably noticed the same handful of tasks eating your week: chasing invoices, answering customer questions, copying data between tools that don't talk to each other. Then "AI agents" started showing up everywhere, from tech newsletters to LinkedIn; you're left wondering whether any of it applies to a business your size. The honest answer is that it usually does, no coding skills required.

Maybe you've already tried a chatbot or a basic automation tool to cut down on repetitive admin. Both helped for a while, then hit a ceiling: chatbots answer a question and stop, while basic automation only runs the exact steps you set up originally. AI agent builders are built to close that gap, letting you create something that can finish a job from start to finish instead of stopping halfway.

What is an AI agent builder?

At its simplest, an AI agent builder is a platform that lets you design, deploy, and manage AI agents without coding them from scratch. The agents themselves run on large language models ( LLMs ) that can reason about a request, decide which tools to use, and carry out several steps toward a goal.

That's the part that separates an agent from a basic chatbot. A chatbot answers what you type into it. An agent can look up an order, update a record, and send a follow-up email from a single instruction, then report back once the job is done.

It helps to see the three side by side.

Tool type

What it does well

Where it falls short

Chatbot

Holds a conversation, answers questions, retrieves information

Rarely takes action inside your other business systems

Rule-based automation

Repeats a fixed sequence of steps reliably

Breaks the moment a task doesn't match the original script

AI agent

Reasons about a goal, chooses tools, adapts mid-task

Needs guardrails, since its behaviour isn't fully scripted in advance

Most of the confusion around "agentic AI" comes from vendors stretching the word to cover all three. A genuine agent is the one that can act, not just answer. Plenty of products marketed as "agents" today are really just chatbots or workflow automations with a new label, so it pays to ask a vendor exactly what their agent can do on its own before you sign up.

How to build AI agents for business automation

Every agent builder, however polished, is made of the same basic pieces. You give it instructions in plain language or a visual flow. The platform connects those instructions to your tools and data through APIs, then handles the reasoning and orchestration behind the scenes.

Most platforms also add memory, so an agent can recall earlier steps in a task. They pair that with guardrails that limit what the agent is allowed to do without a person signing off first.

Builders generally fall into three categories. No-code tools use guided steps and templates built for people with no programming background. Low-code tools expose the underlying logic, so technical teams can adjust it without starting from zero.

Pro-code frameworks such as LangChain sit at the far end, handing developers full control over memory, tool access, and orchestration. IBM's research on enterprise builders suggests the right choice depends less on team size than on how much oversight the work needs once it's live. A small business automating a single email workflow rarely needs the same setup as a bank automating fraud checks.

Why should businesses consider using one?

The case for an agent builder comes down to time and reach. Agents complete actual work instead of just suggesting it. They do this continuously, without adding headcount.

A few concrete reasons businesses are adopting them:

Faster from idea to working tool. Templates and guided builders mean a working agent can go live in days rather than months of custom development.

Lower cost than hiring for every workflow. Consumption-based pricing on most platforms means you pay for what an agent actually does, not for a full-time salary.

Non-technical teams can build their own. No-code builders put creation in the hands of the people who know the workflow best, not only IT.

Scales without adding headcount. One agent can handle a large volume of routine requests at once, freeing staff for the work that actually needs a person.

Agents finish the job, not just start it. An agent can complete several steps across different systems from one instruction, whereas a chatbot would simply answer and stop.

It works for small teams too. You don't need an in-house engineering team to get value; many of the platforms below are built specifically for business owners who aren't developers.

The numbers back this up well. Gartner expects 40% of enterprise applications to include task-specific AI agents by the end of 2026, up from under 5% in 2025. McKinsey has estimated that generative AI, the technology underpinning these agents, could add up to $4.4 trillion a year to the global economy.

None of this happens automatically, though. A 2025 IBM study of 2,000 CEOs found that only 25% of AI initiatives had delivered the ROI leaders expected. Just 16% had scaled across the whole business, which says less about the technology than about how carefully it was deployed.

Common business use cases

AI agents tend to cluster around a handful of jobs. The same patterns show up whether you run a five-person shop or a five-thousand-person company:

Customer service: Answering routine questions, processing refunds, and escalating complex cases to a human when needed

Sales and lead handling: Qualifying inbound leads, drafting outreach, and scheduling meetings

Finance and operations: Processing invoices, chasing approvals, and flagging anomalies in expense reports

IT and HR support: Resetting passwords, answering policy questions, and handling onboarding paperwork

Internal knowledge search: Pulling answers from scattered documents, wikis, and past tickets so staff stop hunting for information

Marketing and content support: Drafting first versions of emails, social posts, or product descriptions for a human to review and publish

Smaller businesses tend to start with one, often customer service or lead handling, before expanding once the first agent proves its worth.

Choosing the right AI agent builder

The right platform depends heavily on your existing tools and your team's technical comfort. If your business already runs on Microsoft 365 , Copilot Studio lets you build agents inside that ecosystem, with internal agent use bundled into Microsoft 365 Copilot licenses and a standalone option for publishing agents outside it.

If your business lives in Salesforce , Agentforce builds agents directly on top of your CRM data, with a free Foundations tier to start and consumption-based credits beyond that. For broader, non-technical automation, tools such as Zapier Agents and Lindy let small teams connect an agent to email, spreadsheets, and thousands of other apps using plain-language prompts rather than code.

Larger organisations weighing governance more heavily often look at platforms such as IBM watsonx Orchestrate, which offers no-code, low-code, and pro-code paths in one place, with centralised oversight over what every agent is allowed to touch. Engineering-led teams that want full control, meanwhile, tend to build directly with open-source frameworks like LangChain rather than a packaged product.

As a general rule, the more deeply your business already runs on one ecosystem, like Microsoft or Salesforce, the more sense it makes to build your first agent inside that same ecosystem rather than bolting on a separate tool. Before you commit to any platform, it's worth getting clear answers to a short list of questions:

Can it connect to the specific tools you already use?

Does pricing scale predictably as usage grows?

What happens when the agent gets something wrong?

Who is responsible for reviewing that?

A few things to consider

Adoption is moving fast, but it isn't risk-free. Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear business value, or weak risk controls. Gartner has also warned of "agent washing," where vendors rebrand older chatbots or automation tools as agents without the underlying capability to match.

An agent is only as good as the data it can see, too. If your records are scattered across spreadsheets, inboxes, and three different apps that don't sync, an agent will struggle no matter how good the platform is. Clean, accessible data is the unglamorous prerequisite that most successful deployments get right before anything else.

Security deserves the same scrutiny you'd give any other system with access to your data.

It's also worth thinking about lock-in before you build anything substantial. Agents built deep inside one vendor's ecosystem, like a CRM or productivity suite, are usually harder to move later than ones built on an open framework or with clear data-export options. That's a reasonable trade-off for the convenience many platforms offer, as long as you go in with eyes open about it.

Start small. Pick one workflow with clear, measurable value. Run it in a sandbox or a limited rollout before expanding it further.

Keep a person in the loop for anything customer-facing or financially sensitive until the agent has proven itself. Revisit that oversight regularly, rather than setting it once and walking away.

FAQ

Do I need coding knowledge to build an AI agent?

Not for most platforms aimed at small businesses. No-code builders use plain-language prompts and visual flows, so a non-technical team member can typically build and launch a simple agent in an afternoon. More complex, multi-system workflows usually benefit from at least some technical input, even if it's just to set up the integrations.

Is an AI agent builder the same as RPA?

No. Robotic process automation follows a fixed script and breaks when something deviates from it. An AI agent can reason about unexpected inputs and adjust its approach mid-task, though that flexibility is also why it needs guardrails that traditional RPA doesn't.

Will AI agents replace jobs at my company?

Most current deployments are built to handle the repetitive parts of a role, not the whole role itself, freeing people for judgment calls and customer relationships an agent can't make. The long-term picture is debated among economists and analysts. Businesses adopting agents should still be upfront with staff about which tasks are changing and why.

What's the difference between an AI agent and an AI assistant?

An AI assistant, the kind of chat tool you ask questions directly, mainly helps a person do their own work faster. An AI agent is built to carry out a task on its own, often across multiple steps and systems, with a person checking in rather than doing every step themselves.