 2026 has become a watershed year for artificial intelligence. The hype around GPT and image generation has given way to a pragmatic question: "Does it make money or not?" And the answer, judging by the market, is yes, it does. But not where people were looking in 2024. I've gathered four trends that are defining the industry right now and broken down what they look like not in consulting firm presentations, but in real business. **Trend one: Multi-agent systems are no longer an experiment** If in 2024 multi-agent systems were talked about as the future, in 2026 they are the present. The n8n platform, for example, showcases networks of AI agents processing over 10,000 tasks per day. Cost reduction compared to a human team is up to 95%. This isn't about replacing people, but about tasks that humans shouldn't have to do: monitoring thousands of logs, coordinating documents across three departments, compiling reports from 15 sources. Multi-agent architecture works like a conveyor belt: one agent collects data, a second analyzes it, a third makes a decision within set rules, and a fourth sends the result. An error at one stage doesn't bring down the entire chain—only the problematic node restarts. This is where the ASI Biont angle lies. Our platform is precisely about assembling such a conveyor belt without hiring a team of developers. You describe the task in natural language, the system itself distributes roles among agents, selects tools, and launches the process. You don't need to write prompts for each link—just say what result you need. **Trend two: Edge AI—intelligence without the cloud** In 2026, an AI model that requires constant internet is considered outdated. Edge AI means the neural network runs directly on the device: on a laptop, a factory controller, a drone. Latency is milliseconds, confidentiality is absolute, cloud computing costs are zero. Real-life example: a manufacturing company near Moscow installed Edge AI on a parts sorting line. The system detects defects in 0.3 seconds without sending a single frame to the cloud. Payback period: 4 months. Before that, defects were caught visually, and missing a defect cost 2 million rubles per batch. For ASI Biont, this trend means we don't tie the user to our server. Agents can work locally, syncing with the platform only for model updates and reports. Businesses that cannot export data externally (banks, defense, medical centers) get intelligence without compromising security. **Trend three: No-code automation of business processes—not for marketers, but for operations** No-code AI has come a long way from landing page builders. In 2026, it's a tool that lets a CFO build a cash flow forecasting model themselves, or a warehouse manager set up inventory shortage predictions without writing a single line of code. Case study: a chain of 40 pharmacies set up an AI agent for inventory replenishment via a no-code interface. The agent analyzes sales history, seasonality, supplier stock levels, and automatically generates orders. Result: a 73% reduction in expired product write-offs in one quarter. Two employees with no technical background implemented it in two weeks. Why this matters for ASI Biont: we've removed the entry barrier. The user doesn't write code, learn Python, or understand APIs. They formulate the task in Russian, the platform selects agents, and the process launches. 1500 tokens at the start are enough to test a hypothesis without investment. If it works, you scale. **Trend four: AI agents as employees, not tools** The most important shift of 2026 is the attitude toward AI. Companies have stopped seeing neural networks as "just another program in the stack." An agent becomes a team member: it has a role, KPIs, access to corporate systems, and the right to make decisions within policies. HR-tech startup Recrewty implemented an AI agent that independently conducts initial interviews with candidates. The agent evaluates not only answers but also tone of voice, response speed, and phrasing. Screening one candidate takes 4 minutes instead of