Incorporate AI agents into your localization project

In a recent post on agentic translation, my colleague Teresa Ortiz introduced a powerful idea: AI agents can now help human translators reason, plan, and interact across languages and channels. But this means translation can’t be an isolated step. It has to be part of how the AI agent reasons, makes decisions, and adapts its behavior across languages.

Now comes an important question: how do we make agentic AI work at the project level? How do we localize not just words, but the reasoning and behaviors behind them? And how do we equip these systems to act with nuance across tone, language, and cultural expectations?

The answer lies in rethinking what localization teams deliver and how they deliver it.

Agentic AI systems operate in dynamic environments. They need to learn, reason, and adapt through interaction. That’s why reinforcement learning with human feedback (RLHF) matters. RLHF turns human judgment into a training signal aligning AI with brand voice, enterprise safety norms, and contextual expectations. It’s a foundation for building agents that communicate with purpose.

That was the basis of my talk at LocWorld 2025, where I joined a panel of my industry colleagues to explore how multilingual AI is evolving. The focus of our track was, specifically, tailoring AI solutions to specific large language model (LLM) translation project needs.

Start with the right question

An agent’s intelligence starts with knowing its goal. That’s why the first step in any localization effort should be defining what the AI agent is trying to accomplish, not just what it’s trying to say. Whether it’s increasing conversions in a new region, improving support flows across languages, or representing brand values through dialogue, the purpose drives the strategy.

Without this clarity, even well-tuned models can fail to deliver impact. I always ask, “What are we optimizing for?” Translation accuracy alone isn’t the right metric. Business impact, user trust, and cultural resonance are what matter.

Incorporate RLHF into your agentic workflows

Agentic AI systems don’t operate in static environments. They learn through interaction and evolve based on feedback. Supporting this evolution requires localization systems that are flexible, iterative, and designed to adapt. That means moving beyond traditional, linear pipelines and instead building tight feedback loops that can update and retrain models in near real time.

One of the most effective strategies for doing this is RLHF. But RLHF is only as strong as the people behind it. Subject matter experts—linguists, cultural strategists, QA leads—need to go beyond editing.

They need to simulate real user experiences, identify confusing outputs that block comprehension or interaction, and label subtle features like politeness strategies, implied meanings, or culturally embedded tones. This kind of feedback teaches the model how to act, not just how to sound.

Speed is important too, but speed alone isn’t enough. Feedback loops must be purposeful, structured, and driven by people who understand how language models learn. That’s why I urged the audience at LocWorld to be curious, resilient, and hands-on with GenAI.

If we don’t understand how these systems actually work, we can’t shape their behavior or guide our teams to work alongside them.

This work becomes even more important as foundational models approach the limits of public training data. Many languages still suffer from sparse or low-quality datasets, and models trained on them often need human reinforcement to improve.

RLHF helps bridge that gap—especially in languages with complex grammar systems or limited digital presence—by embedding real-world human insight where data alone falls short.

Tune for the domain, then the language

For agentic systems to act effectively, domain context comes first. Language fluency is important, but it’s not sufficient. A healthcare agent in Swedish and a retail chatbot in Spanish have very different roles to play, and they need different training data, tone guidelines, and behavior models.

When companies focus only on language support without domain specificity, they risk building agents that sound fluent but act inconsistently. That can erode trust fast. Especially in high-stakes environments—finance, legal, health—the agent’s understanding of context is as important as its command of vocabulary.

This becomes even more critical in rare or low-resource languages.

The MAPS benchmark shows that LLMs often exhibit reduced task performance and safety behaviors in non-English languages, which illustrates the need for more equitable multilingual evaluation. To build truly global agents, we need to treat these languages as core use cases, not edge cases.

Redefine quality

In the era of agentic AI, the definition of quality in localization is expanding. Grammar and completeness still matter, but so do clarity, usefulness, cultural tone, and emotional resonance. These qualities affect not only how the content reads, but how the AI agent acts and builds trust across languages.

At Centific, we embed quality across the entire GenAI lifecycle, not just in the final output. Let's take a look at what that means in practice.

1. Align teams during pre-production through targeted training and tailored workflows

We use micro-learning, gamified training modules, and customized role definitions to align teams from the start. Tailored workflows and clearly defined metrics help ensure quality expectations are consistent across languages and regions.

2. During production, we analyze model outputs alongside reviewer feedback

We examine model outputs alongside reviewer feedback and in-market observations to spot emerging patterns, catch issues early, and shape strategic improvements.

Our six-dimensional quality framework evaluates content across accuracy, authenticity, grammar, natural flow, content variety, and cultural relevance. This captures how well the AI communicates, not just how correct it is.

3. After production, use RLHF to optimize model behavior and drive continuous improvement

We’ve seen major gains from this approach. In one project, retraining a model using 50,000 human-reviewed lines resulted in a 26% improvement in perceived quality. In others, we’ve achieved near-zero false positive rates and recall scores in the high 90s across 15 to 20 languages. These outcomes reflect a system trained to behave with nuance and sound human in context.

That’s why we believe localization today is a systems challenge that determines how effectively AI can operate across languages and cultures.

Localization is now a strategic layer in AI architecture

For agentic systems, localization provides foundational infrastructure. It shapes how the agent thinks, how it responds, and how it builds trust across markets. If localization fails, the agent fails. If it’s done right, localization enables an AI system to act intelligently across cultures.

That’s why we need to integrate localization into every phase of development from training data selection to fine-tuning, testing, and evaluation. Language is how intelligence is expressed.

Centific bridges that gap

We act as language service integrators. We bring together the right annotated data, the right cultural and linguistic expertise, and the right GenAI operation processes to build solutions tailored to each LLM project.

That means rapid well-trained and certified LLM linguistic experts segmented by top domains, scalable quality governance operation models and customized solution platform. And multilingual QA processes built for clarity and trust, not just compliance.

The Centific Flow platform powers this approach. Built on Centific’s frontier AI data foundry, Flow supports rapid data annotation, real-time review, and multilingual quality testing. It helps companies localize their agents from the inside out, creating systems that are not just globally functional, but locally intelligent.

Learn more about Centific Flow, agentic AI for localization.