5 Agentic AI Trends to Watch in 2026

2026 will be a breakthrough year for agentic AI. After a period focused on model sophistication, the spotlight is shifting to how these models actually work in practice – as AI agents that can act, decide, and execute tasks across full workflows.

At Plan.Net Studios, we’ve been tracking this shift closely – and helping clients turn AI theory into real business impact. Here are five trends we believe will define the next stage of agentic AI, and what they mean for marketing, operations, and the future of work.

1. Agents are getting depth – and autonomy

Over the past two years, 'shallow agents' have primarily emerged: specialised AI agents designed for clearly defined tasks such as research, analysis or text generation. In 2026, the technology reaches a new level as several developments converge.

A central foundation is models with very large context windows of around 200,000 tokens – roughly equivalent to 500 pages of text. The context window describes the amount of information a model can keep 'in view' at the same time. Only at this scale can agents grasp complex interdependencies.

On this basis, so-called “deep agents” are emerging. They use tools, can run locally on a computer, and have access to file systems and development tools. When they encounter a problem, they can independently write and execute code. This autonomy turns deep agents into problem solvers that can not only handle small subtasks but iteratively develop complete solution paths. As a result, they resemble autonomous digital professionals rather than classic chatbots.

Anthropic’s 'Computer Use' feature exemplifies the direction this is heading: the agent can control the user’s desktop, open browsers, edit Excel files, build dashboards, and write, test and correct code. Perplexity’s browser with its integrated agent mode 'Come't', as well as OpenAI’s 'Operator', show that the browser layer is becoming the agents’ workplace.

Most marketing use cases benefit significantly from switching to deep agents – because the underlying tasks are highly complex. Projects often require the creation of presentations, Excel spreadsheets, data visualisations or dashboards. Thanks to their programming capabilities, deep agents can generate all of this directly, without humans switching between tools.

2. Agents will soon build themselves

In recent months, many organisations have invested in building their own AI agents. In 2026, manual agent development will increasingly be replaced by automated agent generation.

In short: agents develop other agents. Humans describe the problem they want to solve, and an agent creates the appropriate agent – including structure, tooling and domain-specific knowledge.

This approach is already emerging across the industry. Models such as Claude, Gemini and specialised coding environments increasingly demonstrate autonomous workflow and skill generation.

Humans will assume a curatorial role: output quality will be driven by targeted human fine-tuning rather than complex architectures. Generating agents will be faster and deliver better results than developing them manually.

3. Synthetic knowledge is getting surprisingly good

For a long time, domain knowledge – deep expertise and contextual understanding – was seen as the key human contribution to AI projects. However, tests and research show that a substantial portion of this knowledge can be generated synthetically.

Agents can be improved by deliberately generating domain knowledge. For example for market analyses, competitive research, persona development, industry trends or best practices. Human expertise remains important, but primarily to contextualise synthetic knowledge so that it fits the organisation, cultural context, specific use case or client.

4. We’re shifting from headcount to compute power

Productivity in knowledge work has traditionally been based on human capacity: larger teams, more hours, more project time. But we are reaching a turning point. AI agents can take over entire work steps on their own. Scaling is no longer about increasing headcount, but about deliberately adding compute power.

The new mantra: we should no longer carry out tasks ourselves that can easily be outsourced to agents – instead, we must build the systems that take them over. This is not about full automation, but intelligent relief. When agents take over weekly planning, data preparation or routine analyses, space is created for higher-impact work. We scale through compute, expanding our productive reach.

5. From single agents to orchestrated execution

The most strategically important trend is the shift from individual agents to orchestrated systems. The future does not belong to the best single agent, but to the interaction of many specialised agents acting together like a team.

Instead of linear processes, a dynamic multi-agent architecture emerges where analysis, creation, execution and quality control run in parallel – while humans define goals, set guardrails and stay involved through feedback loops.

A key accelerator is Model Context Protocols (MCPs). Until now, agents had to be individually connected to enterprise systems. MCPs introduce an open standard allowing agents to access tools, data and services in a uniform way, with security and audit trails built in. This standardisation makes it possible to deploy agents reliably across systems and scale quickly.

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The big picture: infrastructure, not just assistance

2026 marks the moment agentic AI evolves from a smart add-on to a foundational productivity layer. Deep agents, orchestration and synthetic knowledge are reconfiguring how digital work gets done.

Organisations that lean in early will unlock more than efficiency gains. They’ll gain speed, quality and capacity – and reshape what marketing teams and businesses are capable of.