Contents
- The new AI model is being pulled from “good at answering” to “able to work”
- What has changed in the AI ecosystem — and how it affects marketing teams’ tool use
- Looking closer: which efficiency metrics are losing value, and what replaces them
- The new AI model in Vietnam: test fast, stay flexible, and don’t lock yourself into one standard
- How to use the new AI model in marketing work
- References
The new AI model is no longer seen as a “better answer machine,” but is being pulled into the real work equation: running agents, connecting to tools, coordinating through plugins, and being accountable for output. For Vietnamese marketers, that matters because the comparison is no longer about how “smart” a model looks in a demo, but which one helps teams work faster, measure more clearly, and depend less on a single platform.
- Key points:
- The value of the new AI model is shifting from answers to execution within workflows.
- The evaluation standard is changing too: not just benchmarks, but agent skills, plugins, integration, and control.
- Marketers need to rethink dashboards, KPIs, and how they measure impact when AI takes over part of the work.
- In Vietnam, the safest strategy is to test quickly while keeping data and processes under control.
The new AI model is being pulled from “good at answering” to “able to work”
The common thread in recent developments is that models no longer stand alone. Gemini 3.5 Pro is rumored to be delayed again, showing that even a major model can lose momentum if it has not solved the product and deployment challenge at the right time (Dan). At the same time, the builder community is talking more about Claude skills, after Andrew Ng and Anthropic released guidance on how to build agentic skills from start to finish (CyrilXBT). In parallel, OpenAI, Cursor, Vercel, GitHub, and Google are all being mentioned in an open Agent Plugin standard, meaning models now need to go together with a connection standard to be widely useful (TestingCatalog).
For marketers, this is a very clear signal: once models start running in real workflows, tool buyers will ask “what can it do” before asking “how well does it talk.” What is emerging is not a contest of sample answers, but a contest to package capability into a system that can take tasks, connect tools, and keep output stable.
What has changed in the AI ecosystem — and how it affects marketing teams’ tool use
The most visible change is that the “pieces” around the model are being standardized faster. Agent Plugins are described as an open standard for building one plugin and using it across compatible clients, while also supporting MCP server configuration in a common format; this reduces the cost of rebuilding integrations for each platform (TestingCatalog). On another track, Claude skills are being pushed as a way to package capabilities for agents, instead of stopping at isolated prompts (CyrilXBT).
Agent Plugins: less rework on integrations, more teams able to share one connection method
When one plugin can run across multiple compatible agent clients, marketing and automation teams no longer have to rewrite the same logic for every environment. That changes how tools are chosen: instead of asking “which model is the strongest,” the more practical question is “can it plug into the existing stack?” For businesses with multiple channels, multiple dashboards, and multiple approval steps, an open standard helps reduce small maintenance work and lowers dependence on a closed ecosystem. Reference source: Agent Plugin standard.
Claude skills: capabilities packaged into workflows, no longer just a single prompt
Claude skills are being described by the community as a way to build “skills” for agents so they can do longer tasks, repeat them more reliably, and follow a clearer process. This directly affects content, research, and ops teams: instead of checking each answer individually, users start caring whether the model can maintain a stable chain of actions. Reference source: Andrew Ng x Anthropic on agentic skills.
Fetch-Skills and ASI:Create: AI tools are also moving toward “packaging for reuse”
Fetch-Skills from Fetch.ai is an open-source CLI that lets users install specialized knowledge into coding assistants like Cursor or Claude with a single command; it is a way to reduce friction when bringing agent capabilities into the working environment of developers and builders (Dami-Defi). In the same source, ASI:Create and Agent Launchpad show agent infrastructure moving toward deployment, management, and self-coordination within a clearer framework. For marketers, this is a sign that the way tools are bought will increasingly resemble buying “a package that gets work done,” not a standalone chatbot.
Looking closer: which efficiency metrics are losing value, and what replaces them
At the analytical level, the biggest change is how the new AI model is evaluated. As OpenAI and partners push Agent Plugins, and Anthropic moves toward a Claude-specific standard for skills, the competitive benchmark is no longer just language performance or a few closed tests (TestingCatalog; CyrilXBT). At the same time, the claim that Gemini 3.5 Pro is delayed is a reminder that the launch speed of a single model is no longer enough to create a sense of safety for enterprise users (Dan).
Single benchmarks are losing strength, because buyers now need to measure workflow fit
Traditional benchmarks only show whether a model answers well on a set of test questions. But when a model is connected to plugins, skills, and an agent framework, what needs to be measured is whether it can maintain a chain of tasks, call the right tools, and return usable results. This is a major change for marketing dashboards: CTR, lead volume, or time-on-task will not automatically reflect true effectiveness if part of the work has already been handled by machines before a human touches it. Put more directly, marketing teams need additional metrics for time saved, the share of work automated, and the number of steps that still require manual correction. Sources that clarify this trend include Agent Plugins and Claude skills.
Launch timing is no longer strong enough proof of product capability
When a major model is delayed, enterprise users no longer look only at the release date. They look at deployment capability, stability, and the surrounding ecosystem. This puts pressure on how marketing teams read the market: they should not chase a launch announcement and immediately switch stacks. They need to ask whether the model already has plugins, skills, integration documentation, and output governance mechanisms. The case of Gemini 3.5 Pro being said to be delayed again, and Google joining open standards at the agent layer, shows that the real competition is about “usable in operations” rather than “sounds powerful on paper” (Dan; TestingCatalog).
Measuring effectiveness must shift from “which model is better” to “which process saves more”
For marketers, what needs to change is not only the tool, but the dashboard. If a new model helps the team research, build briefs, or classify leads faster, the real value lies in fewer labor hours, fewer revision rounds, and faster decision-making. Changes in skills and plugins show that the model is becoming one layer in the process, so reporting has to follow that process too. Reference sources: Claude skills, Agent Plugins, Gemini 3.5 Pro delay.
The new AI model in Vietnam: test fast, stay flexible, and don’t lock yourself into one standard
In the Vietnamese market, the question is not which side “wins” immediately. More important is that marketing and product teams test on a small scale, clearly define which tasks machines are allowed to do, and keep both data and processes at a controllable level. Because standards like Agent Plugins or Claude skills are opening the door to many different environments, Vietnamese businesses should prioritize a stack that can be changed quickly if needed, rather than depending on a single model or a single integration method (TestingCatalog; CyrilXBT).
The reality in Vietnam is that many marketing teams still work in parallel with international tools, internal processes, and very specific reporting needs for bosses or clients. Therefore, the new AI model only becomes truly valuable when it helps maintain execution rhythm, without forcing the team to relearn everything each time a version changes. Signals such as Gemini being delayed or skills/plugins moving toward open standards all point to one thing: flexibility matters more than betting early on a single model name.
How to use the new AI model in marketing work
- Do not judge a model only by a demo response; test it on a real workflow such as research, brief writing, lead classification, or report generation.
- Measure time saved, the number of manual corrections, and the share of work completed by machines, instead of looking only at the final output.
- Prioritize tools with a clear connection standard that can be switched to another system if needed, to avoid being locked into one platform.
- For Vietnamese teams, start small, keep data controlled, and then expand to larger processes.
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References
- DanDr1s: Gemini 3.5 Pro delay claim
- CyrilXBT: Andrew Ng on Claude skills
- Dami-Defi: Fetch.ai Q2 recap
- TestingCatalog: OpenAI Agent Plugin standard
