Most trend lists mix real shifts with wishful thinking. This one sticks to changes that already affect how software products are built, secured and sold in 2026, the kind a startup founder, CTO or product owner should have an opinion on.

Some trends from our 2023 list have moved on. AI chatbots grew into something much bigger, cybersecurity became even more central, and blockchain and augmented reality settled into narrower, practical niches. The list below describes where the industry is now.

For each trend we cover what it is, why it matters and what to do about it.

1. AI coding agents become part of the team

AI coding tools have gone from autocomplete to agents that take a task, read the codebase, make changes across many files, run tests and propose a pull request. Many development teams use tools such as Claude Code, Cursor, GitHub Copilot and OpenAI Codex daily, and some engineers now run several agents in parallel on different parts of a feature.

Teams that use these tools well deliver well-defined tasks noticeably faster. The bottleneck has moved, though. Writing code is cheaper now, and the scarce skills are specifying what to build, reviewing the result and keeping the architecture coherent.

What to do about it:

  • Set clear review rules. Every agent-generated change still needs a person who understands and owns it before it reaches the main branch.
  • Invest in tests and CI. Agents are much more useful, and much safer, in a codebase with good automated checks.
  • Hire or develop engineers who can break work into well-specified tasks and check the output. Our agentic AI engineers work exactly this way.

2. Agentic workflows and the Model Context Protocol

Outside coding, companies are building AI agents that carry out multi-step business tasks: triaging support tickets, preparing reports, reconciling data between systems or drafting documents for someone to approve. For that, agents need safe access to tools and data.

The Model Context Protocol (MCP), an open standard Anthropic introduced in late 2024, has become a common way to connect AI models to external tools and data sources. Major AI providers and many developer tools support it, so one integration can work across several AI clients.

For SaaS companies, an MCP server or a clean, well-documented API is becoming part of what makes a product easy to integrate with. Internally, agents can automate work that used to take custom scripts or manual effort.

Start with narrow, well-bounded workflows where a person approves the result. Give agents the minimum permissions they need, log what they do, and treat any outside content they read as untrusted, because prompt injection is still a real risk.

3. Managing AI costs and choosing models deliberately

Once AI features reach real users, model usage becomes a significant running cost, and it grows with usage, not with revenue. In 2026 more teams handle AI spend the way they handle cloud spend: they measure it, budget for it and optimise it.

An AI feature that looks cheap in a demo can get expensive at scale. Lock-in is a risk too, if one provider changes its pricing or terms.

What to do about it:

  • Track cost per feature and per customer, in addition to the monthly total.
  • Use smaller or cheaper models where they are good enough, and keep the most capable ones for tasks that need them.
  • For suitable workloads, or where data must stay in-house, consider open-weight models on your own infrastructure, served with tools such as Ollama or vLLM.
  • Keep model calls behind your own interface so you can switch providers without rewriting the product. We build our AI and ML development projects this way by default.

4. Cleaning up AI-generated prototypes

With AI app builders and coding assistants, founders can ship a working product without a development team. Many of those products found real users, and many have the same problems: no tests, API keys exposed in the frontend, open database rules, inconsistent structure and code nobody fully understands.

There is still a large gap between a working prototype and a product that can safely handle customer data and payments. Once usage grows, security incidents and constant regressions are common.

Before scaling, get an independent code and security audit, add tests around the critical paths, move secrets out of the code and set up monitoring. In most cases that costs less than a rewrite. Our vibe-coded app support and modernization service does this work.

5. Platform engineering

Platform engineering means building an internal platform that gives developers self-service access to what they need: environments, deployment pipelines, databases, monitoring and security checks, all with sensible defaults. Teams follow one paved path instead of each configuring infrastructure from scratch. Internal developer portals are often built on open-source tools such as Backstage, which started at Spotify.

As teams grow, and as AI agents start creating environments and pull requests too, consistency and guardrails become essential. A platform encodes security and compliance rules once, so they do not depend on every engineer remembering them.

An early startup does not need a platform team. It does need repeatable infrastructure as code, one standard way to deploy, and secrets management from day one. A platform grows from those.

6. Passkeys replace passwords

Passkeys are a phishing-resistant way to sign in, based on the FIDO and WebAuthn standards. The user confirms with a fingerprint, face scan or device PIN instead of a password, and the credential is tied to the specific website or app, so it cannot be typed into a fake login page. Apple, Google and Microsoft support passkeys across their platforms, and passkeys sync between a user's devices. Apple's passkeys documentation for developers is a good place to start.

Stolen and reused passwords are still one of the most common ways accounts get compromised. Passkeys remove that risk, and sign-in is usually faster too.

Add passkeys as a sign-in option in new products, and offer them to existing users next to their current methods. Most major authentication providers already support them, so the effort is usually moderate.

7. Regulation becomes a product requirement

For companies selling in Europe, several new rules now shape what software must do:

  • The EU AI Act entered into force in 2024, and its obligations phase in over several years depending on the type of AI system. Companies building or using AI need to know which category their features fall into.
  • The European Accessibility Act has applied since June 2025 to many digital products and services, including e-commerce, banking services and e-books. For these, accessibility is now a legal requirement as well as good practice.
  • The Cyber Resilience Act sets security requirements for products with digital elements, with obligations arriving in stages over the next few years.

These rules affect design, architecture and documentation, and adding compliance late costs far more than planning for it.

Map which rules apply to your product and markets, get qualified legal advice, and make accessibility, logging and security documentation part of the normal development process. In regulated sectors such as fintech and healthcare, teams already work this way.

8. Software supply chain security and post-quantum preparation

Modern applications depend on hundreds of open-source packages, and attacks that compromise popular packages or developer accounts now happen regularly. AI coding agents add a new risk, since they install dependencies quickly and sometimes suggest packages that do not exist or cannot be trusted.

Meanwhile the move to post-quantum cryptography has begun. NIST published its first post-quantum cryptography standards in 2024, and browsers and operating systems have started adopting quantum-resistant key exchange. The worry is that encrypted data captured today could be decrypted in the future.

What to do about it:

  • Pin dependency versions, review new packages before adding them and run automated vulnerability scanning in CI.
  • Keep a software bill of materials so you know what your product contains.
  • Rely on well-maintained cryptographic libraries and platform defaults instead of custom implementations, so post-quantum upgrades can arrive through normal updates.

How to decide which trends matter for you

You can skip some of these this year. A simple filter:

  1. Does it affect your customers directly? Passkeys, accessibility and AI features usually do.
  2. Does it change your costs or speed? AI coding agents and AI cost management usually do.
  3. Is it required? Regulation in your markets is not optional.
  4. Can you test it cheaply? If so, run a small pilot before committing.

Trends you cannot connect to one of these questions can wait.

Conclusion

The biggest change in 2026 is that AI has moved from a product feature to a way of building software, which raises new questions about review, cost, security and accountability. Familiar themes such as security, authentication and regulation keep growing in importance alongside it. Startups that adopt the useful parts deliberately, and keep people responsible for what ships, get the benefits without taking on the new risks.

If you want a practical view on which of these trends matter for your product, talk to the EmphaSoft team. Our developers use these tools and practices on client projects every day and can help you decide where to start.