AI is changing how apps are built. It helps write code, check how it works, and fix bugs. More tasks can now be assigned to AI agents simply by describing the desired outcome. How far will this go in 2027? Will anyone be able to create their own apps? And what will that mean for businesses and developers? We explore examples from 2026 and explain three terms that are becoming increasingly common: vibe coding, agentic engineering, and agentic architecture.
Three trends stand out:
- moving faster from an idea to the first working version of an app,
- assigning a growing share of development work to AI agents,
- adding AI features that help users with everyday tasks.
A single project can bring all three together: test an idea with a prototype, use agents to develop the app, and give users an assistant that searches internal documents, for example. In 2027, we may see more projects combining these approaches, alongside questions about app quality, the cost of using AI, and responsibility for maintenance.
A few definitions help make sense of these changes:
- Vibe coding – you tell AI what kind of app you want to build, try out the result, and request changes. This lets you build software without examining its code in detail.
- Agentic coding – you give an agent a task, such as adding a registration form. The agent can review project files, plan changes, write code, and run tests.
- Agentic engineering – the team decides how to work with agents: which tasks to assign, what information to provide, and how to check the results. People make the technical decisions and remain responsible for the app’s quality.
- Agentic architecture – agents operate within the finished app. For example, an agent handling a customer request might check records in a business system, find similar cases, and draft a response.
- AI-generated illustration.
Agents can help build an app and also carry out tasks within the finished product. These are two separate uses. A booking system built with agents may run without AI once deployed. Meanwhile, an app with a customer service agent may be developed largely by programmers writing the code themselves.
AI solution building is a broader term. It covers the journey from identifying where AI can help to launching and maintaining a working tool. Along the way, the team needs to select a model, prepare data, connect the app to business systems, and check how it works. It is a general term for building tools that use AI. Coding agents can support development, while the finished app might use document search, content analysis, or an agent that carries out tasks for users.
1. Vibe Coding Will Shorten the Path from Idea to Working App
One advantage of vibe coding is the ability to test an idea before commissioning an app. Someone familiar with the team’s work can use AI to create a simple request form, data dashboard, or task-planning tool. The people who will use the app can test the prototype right away and suggest changes. A working app is easier to assess than a written description of an idea. Even the first tests can reveal oversights: a form asks for information the customer does not have, or requests sit waiting for approval from an absent employee. This is also an opportunity to check with IT which connections to existing business systems are needed. The idea can then be refined before the company commits a larger budget to development.
Real-World Example: FlashNews – an App Built by a Nontechnical Creator
Anyone looking for inspiration to build an app without knowing how to code should take a look at Dan Kempe’s story. With a background in design and marketing, Kempe used AI to create FlashNews – an iPhone app that displays articles one word at a time in a fixed position on the screen. Readers can adjust the pace, choose topics they are interested in, and save articles for later. Kempe used Claude to refine the idea and prepare detailed instructions for Replit Agent. The agent built the app from those instructions, while Kempe tested successive versions on his phone, identified bugs, and requested changes to the design and features. After testing through Apple TestFlight, the app launched on the App Store. Kempe continued developing it after launch, adding options to paste article links, increase the word display speed, and switch between light and dark themes. FlashNews shows how a nontechnical creator can turn a specific idea into a publicly available app, then continue improving it based on user feedback.
2. Requirements Will Become Working Instructions for AI
The more work we assign to AI, the more clearly we need to describe what we want to build. Suppose a company needs an app that lets customers report equipment problems and allows the service team to assign cases to technicians and share repair updates. The idea alone is only a starting point. The team also needs to decide what information customers should provide, who handles each case, and when it can be marked as complete. These decisions can be collected in a document that guides the AI agent through development and gives the team a basis for checking the finished app. This approach is known as spec-driven development – building software from written requirements.
Tools such as GitHub Spec Kit support this process. In April 2026, Thoughtworks described how teams used it to prepare instructions for agents. Alongside feature descriptions, teams provided coding standards and information about the app’s structure. This helped agents make changes that fit the existing software. The approach may become more common in 2027, making clear, up-to-date instructions increasingly valuable. If a company changes its customer service procedures but gives an agent an outdated description, the resulting app may follow the old rules. Requirements therefore need to evolve with the project.
- AI-generated illustration.
3. Teams Will Assign Complete Development Tasks to Agents More Often
An AI agent can already be asked to fix a bug or add a feature. It can review project files, plan changes, write code, and run tests. This opens the door to a different division of work: the developer defines the task, provides the necessary information, and reviews the proposed change. In 2027, knowing how to guide this work may become an increasingly valuable skill. The team needs to define what the agent can change and which conditions the finished feature must meet. It also checks whether the proposed fix resolves the problem without breaking other parts of the app. This is agentic engineering – organizing software development around work carried out by AI agents. Agents also need a suitable working environment, with access to files and tools, ways to check the code, and a record of progress so they can resume after an interruption. Building this working environment is known as harness engineering.
Real-World Example: RV Tech Automates Failure Analysis and Test Creation
At RV Tech, a company developing automotive technology, reporting a vehicle issue in Slack triggers an agent called Devin. It retrieves logs – records of system activity – analyzes the problem, and prepares a report for engineers. Cognition described the process in an article published on May 11, 2026. Agents also help create tests using previously prepared instructions. According to Cognition, RV Tech produced 10–15 tests a day this way, compared with 1–2 when working manually. That figure alone does not establish test quality or time savings across the team. The example does, however, show a specific division of work: the agent prepares an analysis or tests, and engineers review the results.
4. Agents Will Become Part of Business Apps
We have already discussed agents that help build software. They can also operate within the finished app and carry out tasks for its users. An employee or customer describes what they need, and the agent checks the relevant information and uses the available tools. Designing an app like this includes deciding which requests the agent can handle and when it should hand them over to a person. Consider an app for reporting equipment faults. A customer describes the problem, and the agent looks for similar faults and checks the repair history. If information is missing, it asks the customer to complete the report. It can then suggest next steps or gather the details a technician needs to investigate. The company determines which data the agent can access and which actions it can take. This is how agentic architecture works. Some parts of the app follow fixed rules, such as checking customer permissions. The agent chooses its next steps based on the request and the information it finds. To do this, it needs connections to business systems and a way to resume interrupted work.
Real-World Example: Rakuten Connects Agents to Everyday Work Tools
Rakuten uses agents built on Claude Managed Agents across product development, sales, marketing, and finance teams. They are connected to Slack, Microsoft Teams, and an internal task management system. The agents retain information between tasks, while the company selects models based on their effectiveness and operating costs. Yusuke Kaji, who leads business AI initiatives at Rakuten, also stresses the need for people to check the results. Rakuten’s example shows how employees can access agents through tools they already use.
How Should Tasks Be Divided Between Agents?
Designers also need to decide whether to assign a task to one agent or several. In an article published on October 22, 2026, Anthropic describes deployments where bringing more of the work into a single agent improved results and reduced waiting times. Separate agents can be useful for tasks that run in parallel or require an independent review.
In 2027, decisions like these may become a more common part of business app development. Alongside screens and features, teams will need to plan agents’ tasks, their connections to other systems, and employees’ involvement. That division of work should be tested in practice and simplified when additional agents bring no benefit.
5. Tests Will Cover Entire Tasks Carried Out by AI
When an AI agent gains access to business tools, it may change the wrong data or report that a task is complete when it is not. Testing therefore needs to cover the full path from a user’s request to the actual result in the system. In a service appointment app, for example, this means checking that a rescheduled visit appears with the correct date both in the technician’s calendar and in the customer’s message. Teams also need to test what happens when something goes wrong, such as no appointments being available or a connection dropping. Does the agent explain the problem and ask for help, or announce success anyway? These tests need to be repeated, especially after changes to the model or its instructions. They should also confirm that the agent stays within its permissions and waits for employee approval where required. In 2027, assessing an AI app may increasingly mean checking whether its agent completes the whole task correctly, including when it encounters difficulties.
- AI-generated illustration.
6. AI Will Become Part of Existing Business Systems
Businesses already have software for managing orders, billing, and production. Over the years, they have adapted it to their needs and connected it to other tools. This is the environment where AI needs to find its place. Many changes in 2027 may therefore involve gradually extending systems that already work.
Building a new app offers the most freedom. Teams can plan how to work with agents, prepare instructions, and decide which features should use AI from the outset. With an older system, they first need to understand how it works and what it depends on. Even a small change can affect other apps that use the same data. Adding one useful feature can be a good starting point. In a customer service system, an agent could gather the customer’s contact history and draft a response for an employee to approve. This lets the business assess its usefulness in everyday work before assigning it more tasks. Meanwhile, the IT team can use agents to analyze the code and prepare changes to the same app. The pace also depends on the consequences of an error. In a simple tool where a mistake would not disrupt the business, it is easier to try a change and assess its effects. Changes to billing or production control systems need thorough testing and a way to restore the previous version. The extent of AI adoption will also depend on how safely it can be integrated into everyday business operations.
7. More Work Will Go into Reviewing and Refining AI Output
Code can be generated faster than a team can review it. As agents take on larger tasks, the ability to assess their proposals becomes more valuable. Does the change solve the problem? Does it fit the rest of the app? Will someone be able to modify it a few months from now? In 2027, developers may spend a greater share of their time understanding, checking, and refining code prepared by AI.
Getting an app to run does not mean the team understands how it is built. If features keep being added without anyone understanding why the agent chose a particular approach, finding the cause of a bug or assessing the impact of the next change becomes harder. Code reviews and records of key decisions therefore remain necessary when working with AI. How the team checks the code also affects the savings a business actually achieves. A fix generated in minutes may take hours to test and revise, so it is worth calculating the full cost of deploying and maintaining it. That calculation needs to include both AI tool fees and the time the team spends reviewing changes, testing, and fixing problems.
A small pilot can show what this looks like within your own team. Compare similar tasks completed with and without AI, accounting for time spent, the quality of the results, and the revisions needed. This makes it easier to decide which tasks to assign to agents and how much oversight they require.
Discover Where AI Can Improve Your Business
Want to handle requests faster, find information in company documents more easily, or reduce repetitive tasks? The TTMS team can help assess your idea, plan the implementation, and connect the new tool to your business systems. TTMS holds ISO/IEC 42001 certification for its AI management system, confirming a structured approach to managing AI and its associated risks.
Talk to TTMS about implementing AI in your business →
FAQ
Can you build an app without knowing how to code?
Yes. AI can help you build a simple app by letting you describe its features and request changes. More expertise is needed when making it available to users, connecting it to business systems, and protecting data. A working prototype is a good starting point, but it needs to be checked before deployment.
What is the difference between vibe coding and agentic engineering?
With vibe coding, users describe what they need and judge successive versions of an app mainly by how they work. Agentic engineering involves planning tasks for agents, preparing instructions, testing, and reviewing code. It helps teams manage the quality of software developed with AI.
Does an app built by AI agents need to use AI?
No. Agents can build a standard booking system or request form that runs without AI once deployed. Adding an agent to the finished app is a separate decision—for example, to find information or help users complete tasks.
Will AI agents replace developers in 2027?
Agents are likely to take on larger tasks, but the impact on employment is difficult to predict. Teams will still need people who understand the system, can assess the code, and can resolve problems after deployment. The main shift explored in this article is how teams divide their work.
Is building apps with AI cheaper?
It can be, if faster code generation reduces the time needed for the whole project. Savings shrink when the output requires extensive revisions or lengthy reviews. The calculation needs to include the team’s time, AI tools, testing, and ongoing maintenance.
Does adding an AI agent require rebuilding a business system?
Not always. You can start with a single feature, such as searching internal documentation, and connect it to an existing app. The scope of the changes depends on the available integrations, data quality, and permissions the agent needs.
Where should a team start with AI-assisted app development?
Start with a task whose outcome is easy to assess. Measure how long it takes today, then compare that with AI-assisted work, including review and revisions. A pilot like this provides a basis for deciding whether to use agents more widely.
