Something unusual is happening in tech right now. Vastly different companies, from AI labs to project management tools to search engines, have landed on the exact same product shape. The driver is architectural and economic, and it will change the tools, workflows, and expectations for marketers and communications professionals in the near term.

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Everyone is building agents, and they all mean the same thing

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Linear announced two weeks ago that they’re building coding agents. OpenAI is shutting down its video generator Sora and going all-in on Codex. Anthropic is fully committed to Claude Code and Cowork. Notion, Google, Microsoft, Meta: all are building variations of the same thing

Software that can take a goal, use tools, and do work on your behalf. We call it The Great Convergence. The convergence has less to do with models getting better, and more to do with the emergence of the general agent harness. An architecture that Claude Code popularized: a model, a goal, and a set of tools running in a loop. The agent calls tools, evaluates results, and keeps going until the task is done.

What makes this moment different from every previous AI wave is that the architecture generalizes. An agent built to write code can, with the right tools, also write reports, analyze data, handle customer cases, or produce marketing content.

Same loop, different tools. We are not talking about a better feature inside an existing product. We are talking about a new primitive that collapses previously separate software categories into one.

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“We see exactly the same pattern,” says Mimmi Liljegren, Founder & CEO of Ayra. “What used to be separate categories, writing tools, analytics platforms, automation workflows, are merging into one and the same thing. Everyone is building agents that can take a brief and deliver a result.”

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Why everyone is being pulled to the same place

‍Three entirely different types of companies have arrived at the same destination, each for their own reasons.

1. Model companies

OpenAI and Anthropic have started building applications directly. The model layer is

commoditizing fast and margins are under pressure. They need to own more of the value chain, and the agent harness is the most natural application to build on top of their own models.

2. System-of-record companies

Notion and Salesforce already have the data and the workflows. They just need to plug in the agent architecture, and suddenly they can automate the work already happening on their platforms. For them, agents are not a new business, they are a massive expansion of the existing one.

3. Communication platforms

Slack and Teams fit naturally because agents, just like people, need to communicate in order to collaborate. These platforms become the natural orchestration layer for a world where humans and agents work side by side.

Some companies, Microsoft and Google, already span multiple categories, which gives them compounding reasons to build vertically integrated solutions. When Anthropic launches Claude  Managed Agents and Notion integrates it directly into its workspace, we are past the experimentation phase. These are production tools delivering results today.

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The real prize is labor, not software

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This is worth pausing on, because it reframes the entire market. What all of these companies are after is not a new product category. It is the automation of knowledge work itself. When you sell an agent that can do the job of a marketing coordinator, an analyst, or a project manager, you are not selling a tool. You are selling labor. The demand for that has no obvious ceiling. It also explains strategic moves that otherwise seem strange. OpenAI shutting down Sora, a product that generated enormous consumer buzz, makes perfect sense when you realize that enterprise knowledge work is a fundamentally larger market than consumer video generation.

For marketers and communications teams, this reframing matters. The platforms you already use, for content, CRM, project management, analytics, will embed agents that can execute entire workflows autonomously. The question shifts from “which AI tool should we buy?” to “which tasks should the agent own, and which do we keep?”

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The roles and requirements are changing

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We are seeing the emergence of “AgentOps,” a new operational function for managing fleets of AI agents. In marketing teams, early versions are already taking shape: AI editors, prompt librarians, and agent coordinators ensuring that output meets brand standards. Quality expectations follow. When agents can produce content at scale, what differentiates brands is not volume but precision: the right tone, the right facts, the right context. Generic tools produce generic results. Brand-trained systems using RAG and fine-tuning against your own guidelines become a competitive advantage.

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Self-improvement, and the question of quality

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Because the agent harness and its tools are all code, and because language models are exceptionally good at writing code, agents can theoretically improve themselves. They can analyze their own results, identify what went wrong, and write better instructions for the next run. For marketing teams, this means the systems you choose today need to not only be good now, they need to be able to get better over time.

The systems that win are those that learn from every run, every publication, every customer interaction. But here is where it gets interesting: the subjective part. What counts as “good” content? In practice, there is often a gap between what should be produced and what actually gets produced. That is a completely normal part of working as a marketer, but for LLMs and agents, it can be confusing. How do you assess the material when the opinion of what quality looks like is often individual? The answer is not easily given, but it will definitely be discussed further as the possibility of optimizing these systems evolves.

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How marketers should think now

As we see this convergence play out, there are several things to act on already. Let’s go through three of them.

1. Map your workflows.

Identify which tasks are repetitive, rule-based, and data-heavy. Those are the candidates for agent automation. Content production, reporting, channel adaptation, and research are natural starting points.

2. Demand brand training.

Generic AI tools produce generic results. Choose systems that can be trained on your

guidelines, your tone of voice, and your data. That is the difference between a tool you have to teach every day and a colleague who already understands your brand.

3. Keep humans in the loop.

Autonomy is a slider, not a switch. The best teams put agents on execution and keep humans on strategy, quality control, and decisions that require judgment.