Over the past year, it has become increasingly difficult to speak about AI as a single phenomenon or a discrete technology. What emerges instead, when looking across multiple actors and sources, is a shift in how AI is positioned inside organisations. Where it previously functioned primarily as an analytical layer, answering questions, generating content, or supporting decision-making, it is now moving into the execution chain itself.

This is a subtle change in description, but a fundamental change in behaviour.

In retrospect, 2025 can be seen as the year in which the terminology stabilised. Concepts such as agents and copilots began to converge towards a shared understanding. By contrast, 2026 is shaping up as the year in which those concepts are translated into actual enterprise products, governance models, and architectural patterns.

What is most striking about this transition is that it is not driven by a sudden leap in model intelligence. If anything, the more consequential development has taken place around the models. The surrounding stack, tools, integrations, deployment patterns, monitoring, and evaluation, has matured to a point where AI can be used as something more than a response system.

OpenAI now frames agents as systems that move from simple tasks to open-ended workflows, where tool usage, deployment, evaluation, and monitoring are part of a unified structure. Anthropic, already in late 2024, described agentic systems as fundamentally composed of language models combined with retrieval, memory, and tools, while recommending a gradual increase in complexity only when necessary. What has happened since is not a departure from that principle, but its absorption into enterprise products from Microsoft, Google, Oracle, Alibaba, and Salesforce.

What emerges is not a single innovation, but a pattern.

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The first meaningful shift is that action has become the product.

Where previous generations of systems were designed to assist the user, vendors such as Oracle now describe “agentic applications” in which the user expresses a business outcome, and the system gathers data, prepares intermediate steps, and drives the process forward across domains such as finance, HR, and supply chain. Alibaba presents a similar development at the SMB level, where “AI taskforces” take on parts of operational work, albeit with explicit constraints around financial transactions and sensitive data. Google is moving in the same direction within knowledge work, where users are no longer only supported in producing content, but can configure agents that classify, prioritise, and advance multi-step workflows.

In this shift, AI is no longer positioned as a system that answers, but as one that progresses work.

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The second shift concerns the standardisation of the integration layer.

Initiatives such as MCP illustrate this clearly. When Anthropic introduced MCP, it was framed as an open standard for secure, bidirectional connections between AI systems and external tools. Shortly thereafter, Google began supporting managed MCP servers within its own ecosystem, and Microsoft has highlighted similar approaches as a way to create vendor-independent access to tools and data. What appears at first glance as a technical detail is, in practice, a prerequisite for any meaningful execution layer.

Without stable, interchangeable connections to real systems, there is no execution, only isolated experimentation.

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The third shift, and arguably the most consequential, is that identity, approval, and traceability have moved from peripheral concerns to central design elements.

OpenAI’s Agents SDK includes explicit mechanisms for approval of high-impact actions, while emphasising trace grading, monitoring, and evaluation as integral to deployment. Microsoft treats identity as a primary design choice, distinguishing between acting on behalf of a user and acting as a separate entity, and places strong emphasis on observability, logging, and traceability.

These are no longer security features in the margins.

They address a more fundamental question: not whether the system can act, but under whose authority it does so, who approved it, and whether its behaviour can be reconstructed afterwards.

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The fourth shift is the increasing precision around human-in-the-loop.

Previously, this was often invoked as a general safeguard. Now it is being operationalised. Microsoft’s models suggest starting with human review where data quality and risk require it, and only later introducing higher levels of automation. At the same time, more nuanced configurations are emerging, where AI systems can take intermediate decisions within defined boundaries, while humans retain control over critical steps.

Execution is no longer framed as a binary between manual and automated, but as a spectrum.

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The fifth shift is the centrality of simulation and evaluation.

Salesforce, for example, has introduced simulated environments where agents are exposed to large numbers of edge cases before being deployed. Microsoft requires scenario-based evaluations and continuous re-testing. OpenAI integrates evaluation and trace analysis directly into the development lifecycle.

This reflects a change in how these systems are perceived.

Not as features designed to perform well in demonstrations, but as operational systems expected to function reliably in real environments.

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When these developments are considered together, they point to something that is difficult to reduce to a trend.

This is not simply a matter of AI becoming more capable.

It is the emergence of a coherent enterprise architecture for controlled execution.

And when similar structures appear simultaneously across OpenAI, Anthropic, Microsoft, Google, consulting firms, and the security community, it signals that the field is moving beyond experimentation.

It is forming a category.

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This is where the real implication begins.

The central question is no longer whether AI can improve decision-making.

It is how organisations design responsibility, authority, and oversight when systems begin to act within their operational processes.

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This is not, at its core, a technical problem.

It is a problem of execution.

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And for that reason, it is misleading to describe this as the next phase of copilots.

What is emerging is something else.

A layer between intention and action.

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That is where the real tension lies.

Not in the intelligence of the systems.

But in the fact that they are becoming part of the organisation’s actual chain of action.

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