When most people think of AI aiding in everyday translation and localization tasks, they still think of large language models and the language technology platforms that use them. Many also have grown used to the idea of writing instructions, or prompts, for a model to execute: type a prompt, and the AI model generates a translation, a summary, or a piece of code. It’s a powerful but passive relationship with AI as another tool, waiting for us to tell it what to do.
Now, new paradigms are emerging: agentic AI and model context protocol (MCP). The combination is a shift from reactive tools and disparate tasks to a more autonomous AI localization process, where the AI acts as a collaborator that, unlike traditional AI, can make decisions.
While AI agents reason, plan, and take the in…
When most people think of AI aiding in everyday translation and localization tasks, they still think of large language models and the language technology platforms that use them. Many also have grown used to the idea of writing instructions, or prompts, for a model to execute: type a prompt, and the AI model generates a translation, a summary, or a piece of code. It’s a powerful but passive relationship with AI as another tool, waiting for us to tell it what to do.
Now, new paradigms are emerging: agentic AI and model context protocol (MCP). The combination is a shift from reactive tools and disparate tasks to a more autonomous AI localization process, where the AI acts as a collaborator that, unlike traditional AI, can make decisions.
While AI agents reason, plan, and take the initiative to execute complex, multi-step mandates with minimal supervision, MCP is the cherry on top ensuring that context, that elusive yet essential language AI element, is done right in translation.
For the localization industry, this combination is more than just another set of AI upgrades: it’s a fundamental, transformative innovation. Agentic AI doesn’t just translate text; it understands the entire localization workflow. It can navigate code repositories, check design mockups, and apply nuanced brand guidelines and context, aided by MCP —all to ensure that a translation isn’t just accurate but contextually precise. Let’s explore each component separately.
How Crowdin Leverages the Power of Next-Gen Agentic AI
Crowdin has implemented Agentic AI to give localization managers and linguists an intelligent partner that can automate repetitive tasks while enhancing the overall quality and consistency of their work.
Here’s how this next-gen approach is changing the game:
- Agentic AI boosts efficiency by automating several steps, from initial translation to final quality assurance (QA) checks, freeing up valuable time.
- By actively seeking the right context, the AI agent provides translations that are more consistent and accurate, helping to maintain a brand’s unique voice across all languages.
- Managers can now tackle new languages and high-volume projects, scaling localization operations without needing a proportional increase in human resources.
- Agentic AI continuously learns from human feedback and market data, helping localization teams adapt and refine translations to create content with a stronger local relevance.
Agentic AI effectively allows localization managers to transition from task-oriented work to strategic leadership and oversight throughout the entire localization workflow, doing a lot more in a lot less time.
The Model Context Protocol (MCP)
One of the most significant challenges in AI-enabled localization is getting context right. A single word like “charge” can have vastly different meanings depending on whether it appears in a finance app, a mobile game or the UI of a device. Without context, a linguist is left guessing, leading to translation errors and inconsistencies.
Crowdin’s Model Context Protocol (MCP) is a groundbreaking solution to this problem. MCP is a dynamic protocol that allows an AI to connect to and interact with external data sources in real-time. This is different from systems that rely on static, pre-indexed documents, a method known as Retrieval-Augmented Generation (RAG).
While RAG is useful, it lacks the dynamism of MCP, which can fetch information directly from live resources as needed. With MCP, the AI can be instructed to connect to a code repository and check how the string is exactly used in context, and then provide the correct translation and even explain its reasoning.
This approach is several steps above spending time contacting project managers, clients and subject matter experts when there is no other information to convincingly glean meaning. This is the sort of efficient operation that on-point AI automation can offer in Crowdin.
With a meticulously configured MCP rule, before offering a target language equivalent, agentic AI in Crowdin can identify any ambiguities, access connected repositories and search for the relevant key associated with, for example, a UI string (and tell apart a verb from a noun, for example). MCP can even go through comments or usage context indicating what the feature is for.
Important Considerations for Agentic AI
While the potential of agentic AI is immense, it’s not a “plug-and-play” solution, especially in its experimental phase. Crowdin emphasizes the need for careful configuration to get the best results. Users must provide precise, customized rules to guide the AI. Lack of proper guidance can lead to slow responses and unusable output.
For enterprises, the security of sensitive content is paramount. Crowdin addresses this by prioritizing data ownership and privacy. Users can rely on their own API keys for leading AI providers, and Crowdin has secured contracts with its AI partners to ensure that your data is not used to train or improve their models. This guarantees that your information remains confidential.
The ideal outcome is not to replace human experts but to empower them with AI collaborators. Agentic AI can handle tedious tasks that consume much of a localization professional’s time, allowing them to productively focus on the creative, strategic, and culturally nuanced aspects of their work.
The era of agentic AI is here, and it should be understood as a partnership —a collaboration between human expertise and a truly intelligent system.
For Crowdin users, this is about a new localization matrix where AI doesn’t just follow prompts; it understands your goals and actively works to achieve them. To start using Agentic AI in your next translation project, be sure to visit the Crowdin store and get 50 free prompts per month. If you don’t have a Crowdin account yet, please register today!
Crowdin is a leading AI-powered localization platform designed to accelerate the management of multilingual content. By connecting with over 600 tools, Crowdin enables teams to localize apps, software, websites, games, help documentation, and designs, delivering a native experience to customers around the world.
