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

Some of the 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 reflects where the industry actually 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 moved from autocomplete to agents that can take a task, read the codebase, make changes across many files, run tests and propose a pull request. Tools such as Claude Code, Cursor, GitHub Copilot and OpenAI Codex are used daily by many development teams, and some engineers now run several agents in parallel on different parts of a feature.

Why it matters: the speed of delivery for well-defined tasks has increased noticeably for teams that use these tools well. At the same time, the bottleneck has moved. Writing code is cheaper. Specifying what to build, reviewing the result and keeping the architecture coherent are now the scarce skills.

What to do:

  • Set clear rules for review: every agent-generated change still needs a human 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

Beyond 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 human approval. To do that, agents need safe access to tools and data.

The Model Context Protocol (MCP), an open standard introduced by Anthropic 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 now support it, so a single integration can work across several AI clients.

Why it matters: for SaaS companies, offering an MCP server or a clean, well-documented API is becoming part of being easy to integrate with. For internal use, agents can automate work that used to need custom scripts or manual effort.

What to do: start with narrow, well-bounded workflows where a human approves the result. Give agents the minimum permissions they need, log what they do, and treat any content they read from outside as untrusted, since prompt injection remains 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 scales with usage rather than with revenue. In 2026 more teams are treating AI spend the way they treat cloud spend: measured, budgeted and optimised.

Why it matters: an AI feature that looks cheap in a demo can become expensive at scale. Vendor lock-in is also a risk when one provider's pricing or terms change.

What to do:

  • Track cost per feature and per customer, not just the monthly total.
  • Use smaller or cheaper models where they are good enough, and reserve the most capable ones for tasks that need them.
  • Consider open-weight models hosted on your own infrastructure, served with tools such as Ollama or vLLM, for suitable workloads or where data must stay in-house.
  • Keep model calls behind your own interface so you can switch providers without rewriting the product. Our AI and ML development projects are built this way by default.

4. Cleaning up AI-generated prototypes

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

Why it matters: the gap between a working prototype and a product that can safely handle customer data and payments is still large. Security incidents and constant regressions are common once usage grows.

What to do: get an independent code and security audit before scaling, add tests around the critical paths, move secrets out of the code and set up monitoring. In most cases this is cheaper than a rewrite. This is the focus of our vibe-coded app support and modernization service.

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. Instead of every team configuring infrastructure from scratch, they follow a well-paved path. Open-source tools such as Backstage, originally created at Spotify, are often used to build internal developer portals.

Why it matters: 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, instead of relying on every engineer to remember them.

What to do: an early startup does not need a platform team. It does need repeatable infrastructure as code, a single standard way to deploy, and secrets management from day one. Those are the foundations a platform grows from.

6. Passkeys replace passwords

Passkeys are a phishing-resistant way to sign in based on the FIDO and WebAuthn standards. Instead of a password, the user confirms with a fingerprint, face scan or device PIN, 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 they sync between a user's devices. Apple's passkeys documentation for developers is a good starting point.

Why it matters: stolen and reused passwords remain one of the most common ways accounts are compromised. Passkeys remove that risk and usually make sign-in faster.

What to do: add passkeys as a sign-in option in new products and offer them to existing users alongside 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, with obligations phasing 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. Accessibility is now a legal requirement for these, not only good practice.
  • The Cyber Resilience Act sets security requirements for products with digital elements, with obligations being introduced in stages over the next few years.

Why it matters: these rules affect design, architecture and documentation. Adding compliance late is far more expensive than planning for it.

What to do: map which rules apply to your product and markets, get qualified legal advice, and build accessibility, logging and security documentation into the normal development process. In regulated sectors such as fintech and healthcare, this is already standard practice.

8. Software supply chain security and post-quantum preparation

Modern applications rely on hundreds of open-source packages, and attacks that compromise popular packages or developer accounts have become a regular occurrence. AI coding agents add a new angle, since they can install dependencies quickly and sometimes suggest packages that do not exist or are not trustworthy.

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

What to do:

  • 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.
  • Use well-maintained cryptographic libraries and platform defaults rather than custom implementations, so upgrades to post-quantum algorithms can arrive through normal updates.

How to decide which trends matter for you

Not every trend deserves your attention 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 yes, run a small pilot before committing.

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

Conclusion

The defining shift of 2026 is that AI has moved from a product feature to a way of building software, and that brings new questions about review, cost, security and accountability. Around it, more familiar themes such as security, authentication and regulation keep getting more important. Startups that adopt the useful parts deliberately, and keep humans responsible for what ships, will get the benefits without 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.