From Prompting to Partnering: Building Agentic AI Apps for Localization
- Minting Lu
- Oct 26
- 4 min read
Updated: Oct 27
By Minting Lu, GlobalSaké Intern
Over the past month, I’ve been taking the LocLearn Upskill course on Building Agentic AI Applications for Product and Localization. Inspired by this course, I wanted to capture some of the core ideas and how they reshaped my thinking about AI in Localization.
So far, we have explored several foundational concepts: Agentic AI vs. Generative AI, Retrieval-Augmented Generation (RAG), Focused AI, Explainable AI (XAI), Guardrails, and Agile & Adaptable AI.
Each concept reveals a new way to think about AI not as a tool that simply produces output, but as a collaborator that reasons, retrieves, validates, and learns with us.
Agentic AI vs. Generative AI
Generative AI creates content. Agentic AI creates outcomes.
In the course, we broke Agentic AI down into its core components: a brain (the language model), memory, a knowledge base, and tools. Together, they form a system that can make decisions, use external resources, and validate results.
Coming from a localization background, this framework felt surprisingly familiar. We already work with multi-layered systems: translation memories, term bases, reviewers, and engineers. Agentic AI simply formalizes that logic into machine-readable workflows. It is not replacing human reviewers. It is designing workflows where AI takes care of the mechanical checks and humans bring judgment and cultural insight.
Retrieval-Augmented Generation (RAG): Let AI Open Its Notebook First
Instead of letting the model invent answers, we first let it look things up—retrieve the most relevant pieces of knowledge and then respond. I like to imagine it as an AI opening its notebook before speaking.
In one of our n8n practice sessions, we built a RAG workflow that pulled information from uploaded documents, extracted key segments, and then generated precise summaries. I experimented with different models to see how each handled the task.
I also learned how much impact a well-designed system prompt can have. By setting clear expectations such as asking the AI to base every answer strictly on the uploaded text, summarize objectively, and acknowledge when something was not mentioned, I could steer its tone and discipline its reasoning.
That exercise taught me that context alone isn’t enough. Good agentic design also depends on rules of engagement.
Focused AI: Building Depth Instead of Breadth
Another important concept we explored was Focused Agentic AI—a way to make AI perform with precision rather than generality.
At the design level, we guide the agent through structure. In workflow design, we define its role through a system prompt (such as “you are a professional reviewer” etc.), provide context through RAG, and connect the right tools for execution. These boundaries keep the agent anchored within a clear functional frame instead of wandering into unrelated reasoning.
At the model level, focus can also come from specialized training. Models fine-tuned on large, domain-specific datasets become exceptionally strong in one application area. For instance, a model trained for transcreation, cultural adaptation, or LQA can outperform general-purpose LLMs in those targeted scenarios.
The strength of Focused AI lies in depth over breadth. Rather than trying to do everything at once, it excels within a well-defined purpose, delivering outputs that feel more accurate, contextual, and trustworthy.
Explainable AI (XAI): Making AI’s Thinking Visible
This was the most thought-provoking part of the course for me. XAI is all about transparency. It helps humans understand why AI makes certain decisions, not just what those decisions are.
Real-world examples bring this idea to life.
PayPal uses XAI to detect fraudulent transactions and show analysts which behavioral patterns triggered each alert, improving trust in automated risk scoring.
Google DeepMind, in collaboration with Moorfields Eye Hospital, built an AI system that helps detect eye diseases from retinal scans and shows doctors which parts of the image led to its diagnosis, making results easier to explain to patients.
Autonomous driving systems rely on XAI to justify real-time decisions such as braking, lane changes, or hazard detection, allowing engineers to audit system reasoning after each run.
All of these cases share a common truth: trust comes from traceability.
When we understand why an AI acted a certain way, we are more willing to collaborate with it.
The same principle applies in localization. If a translation model replaces a phrase or shifts tone, we should be able to see the reasoning behind it. One example comes from Translated’s “Lara” system, which incorporates explainable AI methods such as attention visualization and decision path analysis to reveal how the model interprets linguistic context. This kind of visibility builds transparency and trust between human linguists and AI, making collaboration far smoother.
From Theory to Practice
Building workflows in n8n made these ideas tangible.
We experimented with translation proofreaders, contextual QA agents, and SEO keyword extractors. Watching each step unfold helped me understand how agentic design actually works in practice.
After the class, I felt inspired to explore other automation platforms like Zapier and see how they could integrate similar AI logic. The more I experiment, the more I realize that these tools are not just about efficiency but about learning how to think in systems.
In a fast-changing AI landscape, the moment you start learning is already the moment you begin to upskill.
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