The software development industry changes by leaps and bounds; it impacts your plans for developing digital products or transforming current systems. Knowing the key software development trends will help you make smarter, future-proof decisions before searching for a software vendor or implementing changes into current workflows using emerging technologies.

We’ll discuss agentic AI, the spec development process, the evolution of low-code platforms, and other key trends, which will shape budgets, timelines, development strategies, and the long-term viability of your solution. Understanding where the industry is heading gives you a strategic advantage in the software development market. Let’s explore what will impact software development in 2026 and how to use these changes for the benefit of your next digital project.

Spec-Driven Development vs. Vibe Coding

When you want to create a software product or engage a vendor to transform your existing infrastructure, you are seeking a cost-saving solution. Many clients think that AI tools can solve it all and that traditional software development practices have fallen behind.

Spec-driven development has been utilized to support numerous enterprise systems, mobile applications, and web platforms. The idea is to inform developers precisely how to implement the system and provide the team with acceptance criteria to meet. 

This structured approach has always helped build reliable systems with a consistent implementation flow, as changes are carefully managed and nothing is deployed to production without passing a thorough code review and Quality Assurance. However, nowadays, this bulletproof method is considered slow. 

Writing and revising specs, then coding and testing against them, takes time. If requirements change mid-project, a traditional workflow might require time for re-planning and refactoring. Changes are costly.

Artificial intelligence development offers several approaches, including using advanced AI systems (such as GitHub Copilot, OpenAI’s Codex, and Amazon CodeWhisperer) to generate or assist with code, testing, and other development tasks. Developers use prompt-based AI assistants, powered by natural language processing or high-level instructions, and receive code suggestions or even entire functions in return.

Spec-driven development is becoming a key best practice in AI-augmented development. Instead of asking AI to write code based on a prompt, teams write a clear specification first. Business Analytics gives developers the user stories and acceptance criteria they need to work on. You can still experience “hallucinations” when using AI during the app development process. This means AI can write code that doesn’t align with your real goals. In a spec-driven AI workflow, Business Analysts write detailed specifications that tell AI what to do.

Why Spec-Driven Development matters

• AI models follow well-defined rules more reliably
• Requirements become unambiguous
• Code generation becomes repeatable and scalable
• Feature changes are implemented faster and with fewer errors
• Documentation, architecture, and code stay aligned

Examples/Tools: New AI-based IDEs (e.g., AWS’s Kiro agentic IDE) support both modes: a vibe mode for brainstorming code and a spec mode to enforce design contracts. Traditional tools like UML or API-first frameworks provide spec-driven support. Vibe coding often uses AI copilots (GitHub Copilot, Amazon CodeWhisperer) or ChatGPT plugins to generate and refactor code.

Agentic AI for Business

Agentic AI refers to autonomous or semiautonomous AI systems that plan and execute multi-step tasks without human prompts. In software development, AI-powered tools analyze codebases, plan development tasks, generate code, or run tests and iterate on results.

AI Agents

AI agents are autonomous or semiautonomous software entities that use AI approaches to perceive, make decisions, act, and reach goals in their digital or physical contexts. Although AI agents keep improving and changing workflows, the way you implement them still depends on the requirements and context.

• AI agents break down high-level goals into actionable tasks and keep executing them until you get the desired outcome.
• Agentic AI can manage infrastructure, do coding, optimize data pipelines, handle customer support flows, or monitor system performance.
• Companies can handle and manage more by using AI agents that work 24/7.
• Still, some tasks require human oversight due to compliance, safety, or trust concerns.
• AI agents are powerful but not a replacement for product strategy, engineering, or domain expertise.

AI-Ready Data: The Basic

AI-ready data are datasets that are ready for AI applications; i.e., they should be adapted for AI use cases. It can only be decided contextually, based on the AI use case and the applied technique, which necessitates advanced data management approaches. For businesses, this means:

• evolving data management practices,
• ensuring data governance and compliance,
• protecting IP and sensitive data,
• preparing structured datasets tailored to each AI use case.

In other words, you cannot have capable AI agents without properly prepared data.

Multimodal AI as the Next Layer

Multimodal AI models are trained with a variety of data types, including images, videos, audio, and text. These models can handle complex tasks by integrating and analyzing multiple data sources. Modern AI agents are becoming multimodal to help:

• understand real-world situations more accurately,
• support valuable interactions,
• make product features advanced.

AI TRiSM (Trust, Risk, and Security)

AI has become deeply integrated into software solutions, security tools, and automation workflows, but with AI agents taking actions autonomously, the risks increase. Businesses must ensure that AI is reliable and ethical to comply with regulations and build trust with users, clients, and partners. AI TRiSM protects companies from:

• unauthorized data exposure or privacy breaches,
• inconsistent results or model drift,
• hallucinations,
• compliance violations across industries (GDPR, HIPAA, PCI, EU AI Act)
• reputational damage caused by unethical AI outputs.

Example: How LaSoft Builds HR-Focused AI Agents That Transform People Operations

Agentic AI becomes valuable when it solves real-world business problems; Lasoft takes a product-engineering approach to AI agents. Let’s explore, as an example, how our fully customized HR AI agent automates operational work, supports employees, and enables HR leaders to access data and insights, providing real visibility into internal processes and team interactions.

A Strategic AI Partner for HR Teams

Unlike generic chatbots or out-of-the-box assistants, Clara is an agentic solution, customized for your specific needs and integrated into your HR ecosystem, capable of:

• manage your internal processes and data,
• integrate HR tools and manage workflows,
• making informed decisions based on your policies,
• assess the possibility of flight risk of team members,
• help with onboarding and interactions with personnel,
• work safely with sensitive data,
• adapt to unique workflow requirements.

Want an AI agent tailored to your business process?
Send your question, and we will propose a free architecture idea.

Low-Code and No-Code Development

As technology advances, low-code and no-code platforms are becoming increasingly popular. These solutions offer a promising way for businesses to build applications and automate processes quickly without extensive coding knowledge. However, low-code and no-code development also present several challenges.

1. One of the most significant concerns is the risk of creating inefficient or poorly designed applications due to the lack of technical expertise needed to use the platforms. Additionally, these platforms may not meet the complex needs of larger enterprises. This implies that the app cannot be subject to monitoring, data generation, or exposure to inappropriate users or the general public.
2. Compromise may also affect the system’s security and scalability. Often, the low-code system is designed to quickly create a prototype and validate the market. Low-code systems are not robust, secure, or scalable solutions.
3. The user interface of these systems is often basic or even primitive.
4. Intellectual Property Rights. Typically, securing funding requires retaining all IP rights for your project. In the case of low code, the IP for the developed solution code does not belong to you.
5. The quality and maintainability of the code are not your responsibility. Low-code and no-code solutions are universal in nature, meaning the code is universal but not optimized for later maintenance performance.

Low-code and no-code systems are helpful in many ways. Professionals often use low-code platforms to build custom apps or MVPs. In contrast, a no-code platform usually enables business users to optimize their upfront budgets and test their business idea in the market. The issues and concerns arise when your product becomes a success and needs to scale.

Where LCNC solutions often fail

• Scaling limitations once the user load grows
• Performance issues for complex logic
• Poor integration with custom systems
• Lack of flexibility for advanced AI or agentic workflows
• Vendor lock-in and expensive extensions
• Difficult maintenance for data-heavy apps.

How Lasoft Helps Companies Move Beyond Low-code/No-code

Lasoft works with teams that:

• outgrew their initial no-code MVP
• need custom integrations, impossible in LCNC tools
• want to add AI features, agents, dashboards, or workflow automation
• require a stable, scalable, production-ready solution.

We help with:

• rebuilding no-code prototypes into full custom platforms
• integrating LCNC flows with custom modules
• adding AI intelligence to LCNC systems
• migrating from no-code tools to scalable architectures.

Vertical AI – Industry-Focused AI Solutions

Vertical AI, as a software development trend, refers to purpose-built AI systems for specific sectors, such as healthcare, HR, finance, insurance, logistics, or telecom. Instead of training a single model to handle general requests, the software development team builds or selects AI models optimized for a business’s domain, workflows, terminology, compliance requirements, and data patterns.

At LaSoft, we specialize in creating custom AI agents designed around a client’s sector, internal processes, data ecosystem, and regulatory requirements. Our experience across HR, healthcare, insurance, logistics, telecom, and digital marketplaces allows us to build AI that speaks your language with your internal team and clients.

When Vertical AI Is the Right Choice

✔ your industry has specific rules and compliance
✔ you want to automate structured workflows
✔ accuracy is more important than creativity
✔ you need integrations with existing enterprise systems
✔ decisions require transparency and traceability

Cloud Cost Optimization: Smart Companies Move from AWS to Cost-Efficient Cloud Platforms

Cloud infrastructure used to be a simple choice: everyone went to AWS, Azure, or Google Cloud because they offered scale, reliability, and convenience. This model was effective for years, but we believe we have reached a significant turning point. As companies increasingly rely on AI workloads, data-heavy applications, automation services, and 24/7 digital products, something becomes painfully clear: traditional cloud spending is rising faster than the value companies are getting from it.

As cloud prices rise and AI workloads increase, companies are rethinking their infrastructure strategies. A major trend for 2026: moving from large cloud providers (AWS/GCP/Azure) to more cost-efficient alternatives, especially for predictable workloads. We mean here software engineering optimization.

Why companies are migrating

• AI workloads require GPUs that are expensive on AWS,
• Storage and bandwidth costs rise every year,
• Many services are overkill for small/medium teams,
• Predictable workloads run perfectly on simpler infrastructure,
• 30–60% cost savings are achievable with no performance loss.

AI-Assisted Quality Assurance

QA engineers are now using AI systems to improve automated testing and debugging, helping speed up software delivery. AI testing tools can create test cases, verify UI flows (using visual AI), and even forecast potential bugs. This leads to a shift from manual scripting to a smarter, data-driven QA process.

Faster development lifecycles require quicker QA, and many experts depend on AI to keep pace with CI/CD speeds. AI can significantly cut testing time; some vendors claim 50–90% faster cycle completion. Additionally, AI can detect bugs that humans might miss by analyzing large codebases or simulating various user behaviors. Industry reports indicate that companies using AI testing experience far fewer defects reaching production. AI also plays a role in debugging: ChatGPT-like agents and plugins analyze code semantics to identify errors or security flaws.

Green Software Engineering (Sustainable Coding)

Green software engineering (or “green coding”) focuses on designing and developing software to minimize energy use and carbon emissions. Techniques include reducing code bloat, optimizing algorithms, and choosing efficient data structures. It overlaps with green IT (efficient data centers, containerization) but specifically emphasizes the software layer, e.g., lean code practices, minimizing wasted cycles, and using event-driven architectures. The Green Software Foundation (founded by Microsoft, GitHub, Accenture, etc.) is publishing standards and metrics for “carbon-aware” development.

The global focus on climate means companies will increasingly track and reduce their IT emissions. Software alone accounts for a few percent of global greenhouse gas emissions, and as AI/compute demand grows, its share will rise. Sustainability goals and regulations (e.g., ESG reporting requirements) push companies to develop carbon strategies and optimize software for energy efficiency. For example, choosing efficient languages (C and Rust often use less energy than Python for compute-heavy tasks) and minimizing wasted compute (turning off idle servers, using serverless) show positive results for companies that follow sustainable coding practices. Tools like IBM’s green coding guidelines encourage minimizing lines of code and optimizing data access.

Ethical AI in the Development Process

Let’s explore “human-centered AI,” which means keeping people (users, software developers, and society) at the core of AI systems. This encompasses ethical AI principles: fairness (avoiding bias in model outputs), transparency (explainability of AI decisions), privacy, and accountability. In development workflows, it also means designing AI features with user needs and values in mind and ensuring AI tools assist rather than replace human creativity. It includes practices such as inclusive design, diverse training data, and embedding ethics checks into code reviews.

With AI-driven features everywhere, stakeholders are demanding responsible AI. Regulations (e.g., the EU AI Act and various national guidelines) will require companies to certify their AI products for bias and safety. Clients and users are increasingly aware of AI risks (e.g., disinformation, privacy), so trust becomes a key differentiator. In short, “AI ethics” won’t be a side concern but a standard part of building software.

Edge AI and Model Compression

Edge AI refers to running AI models directly on devices (sensors, cameras, phones, and industrial equipment) rather than relying on cloud inference. To enable this, large neural networks must be compressed (quantized, pruned, and distilled) so they fit the limited compute and power of edge hardware. Model compression techniques (lower-precision math, weight pruning, and knowledge distillation) shrink models by 4–8× or more without severely degrading accuracy. The result allows sophisticated features (image recognition, NLP) to run on-device in real time. Edge AI also includes new specialized hardware (NPUs and neuromorphic chips) that accelerate inference at low power.

Many applications will demand on-device intelligence, e.g., real-time machine vision in factories, personalized AR on glasses, and offline voice assistants in cars. Emerging 5G networks and on-chip AI accelerators make this practical. Data-sensitive fields (healthcare, finance) also favor keeping data local. The trend is that “AI models are shrinking to fit edge devices” as a proven practice. Many frameworks and hardware support this trend.

TensorFlow Lite, PyTorch Mobile, ONNX Runtime, and Apple’s Core ML allow developers to deploy models on mobile/IoT devices. Products: self-driving cameras running quantized vision models; smart watches using tiny ML for health tracking; routers embedding voice assistants. Notable are “small” transformer models (TinyBERT, DistilGPT) designed for the edge. In short: expect most AI toolkits to include an “export to edge-friendly model” feature, and AI pipelines to incorporate compression steps.

Conclusion

The future of software development is more dynamic and offers opportunities for those working in the market. For business leaders planning new products or evaluating software teams, understanding these key trends is no longer optional. It directly influences the cost, speed, quality, and long-term sustainability of your digital initiatives. The professional technology vendor has expertise in integrating AI agents, rebuilding an MVP into a scalable platform, or optimizing your infrastructure to reduce costs.

At LaSoft, we help companies navigate this transformation by combining engineering expertise, AI-driven innovation, and deep domain knowledge across HR, healthcare, finance, logistics, martech, real estate, telecom, etc. If you’re preparing for your next digital project, exploring automation, or modernizing legacy systems, now is the ideal time to align your strategy with the technologies defining the future.

FAQs

When is Agentic AI the right choice?

It provides the most value when your workflows involve repetitive, multi-step tasks that follow a clear structure and can be safely automated. It’s ideal for:

• routine operational work that takes up significant staff time,
• requiring 24/7 interaction and communication,
• data analysis involving large amounts of structured or semi-structured data,
• tasks with well-defined rules and specifications,
• scenarios where AI can enhance domain expertise.

However, if a workflow demands complex human judgment, empathy, negotiations, or legally sensitive decisions, agentic AI should support the process while humans lead it.