Agentic AI development in Vietnam: Ultimate Enterprise guide

Deloitte forecasts that the share of enterprises using GenAI to deploy AI agents will rise from 25% in 2025 to 50% by 2027. Against this backdrop, Agentic AI development in Vietnam is gaining increasing attention, supported by the country’s seventh-place ranking in the 2023 GSLI and its competitive development costs. However, is Vietnam truly ready to build production-grade systems? This article examines the country’s capabilities, cost advantages, and leading development partners.

1. Why Vietnam for Agentic AI development? 

Vietnam is an ideal destination for Agentic AI development, supported by three strong foundations: a steadily expanding technology workforce, a clear national AI strategy, and extensive experience delivering software that meets international standards. More specifically:

1 - Vietnam's AI talent pool: engineers, labs, and national strategy

According to UNESCO’s 2025 AI Readiness Report, Vietnam currently has approximately 5,000 – 7,000 AI engineers and a network of more than 50 universities offering AI-related programs. These institutions supply the market with 50,000–70,000 IT graduates each year. This highly skilled workforce is concentrated primarily in Hanoi and Ho Chi Minh City, which together account for around 82% of the country’s technology talent. Meanwhile, major data center infrastructure is concentrated in Hanoi, Ho Chi Minh City, and Da Nang.

At the national level, Vietnam’s AI Strategy through 2030 aims to position the country among the top four in ASEAN and the top 50 worldwide in artificial intelligence. At the same time, increasing attention is being given to UNESCO’s recommendations on data governance, AI safety, accountability, and ethics. These efforts provide an important foundation for implementing robust security controls and guardrails in Agentic AI systems.

Vietnam’s delivery capabilities are clearly demonstrated by TMA Solutions, whose key strengths include:

• 4,000 engineers: Serving clients across 30 countries, TMA has the capacity to deliver large-scale projects that require close coordination across AI, data, cloud, security, and enterprise integration.

• Telecom Software Center: Backed by 29 years of experience and a team of 2,000 engineers specializing in system architecture and core networks, the center helps ensure performance, availability, and security at scale.

• Dedicated AI Center: With more than 10 years of experience, the center has successfully delivered over 100 AI projects and 100 AI solutions across more than 20 countries.

Building on these capabilities, TMA delivers end-to-end AI solutions, from consulting and AI transformation to AI product development and AI as a Service. Its expertise covers generative AI, AI Agents, data analytics, natural language processing, smart camera, and RPA, enabling TMA to develop AI solutions tailored to different business needs and processes.

Vietnam’s AI talent pool and national strategy

2 - Cost advantage without quality compromise

Vietnam offers a highly competitive cost base, particularly when compared with the United States and Western Europe. However, any comparison should consider the same outsourcing model, engineer seniority level, and scope of services. Agentic AI projects may also cost more when they involve complex architectures, stringent security requirements, or integration with core enterprise systems.

Note: The rates above are provided for market positioning purposes only. Actual costs may vary depending on each provider’s capabilities and pricing policies. For an accurate and cost-effective estimate tailored to your project, you should contact potential implementation partners directly.

Clutch data indicates that outsourcing rates in Vietnam and India typically range from USD 25 to USD 49 per hour, making them significantly more cost-effective than higher-priced markets such as the United States and Germany. On average, choosing Vietnam can reduce development costs by up to 70% compared with the United States and 50% compared with Germany.

However, Vietnam’s appeal extends beyond lower hourly rates. In the 2023 Kearney Global Services Location Index, Vietnam ranked seventh among 78 countries, reflecting its strong talent pool, digital capabilities, and business environment.

At TMA, these advantages are further reinforced by internationally recognized quality management standards, including CMMI Level 5, ISO 9001:2015, and ISO/IEC 27001:2022. These frameworks help businesses minimize defects, security risks, and remediation costs when deploying AI agents in production environments.

The advantages of AI outsourcing in Vietnam

2. Industries driving success with Agentic AI in Vietnam

Agentic AI is reshaping operations across several of Vietnam’s key industries. Below are real-world projects delivered by TMA Solutions, with measurable business outcomes.

2.1. Agentic AI in telecom

The client is a Canadian telecommunications service provider specializing in contact center solutions for the e-commerce, retail, and logistics sectors. The company was struggling with rapidly increasing call volumes, inconsistent response quality, and lengthy onboarding periods for new agents. Its biggest challenge was maintaining a high standard of customer service while reducing staffing requirements and optimizing operating costs.

TMA’s approach: TMA applied Generative AI and large language models (LLMs) to develop an Intelligent Contact Center Assistant:

• Speech-to-Text: The system analyzes customer inquiries during live calls in real time and recommends appropriate responses to contact center agents.

• AI-powered knowledge base: OpenAI GPT and LlamaIndex were integrated to process enterprise data, enabling the system to retrieve and deliver accurate, concise answers.

Results:

• 30% reduction in call handling time compared with the traditional contact center model, following an initial 20% improvement during the early implementation phase.

• 20% reduction in contact center staffing requirements, generating significant operational cost savings.

• More consistent customer responses, with AI providing accurate, standardized, and real-time information.

• Higher agent productivity, as employees can respond more quickly using AI-generated recommendations, resulting in an improved customer experience.

Case study: Optimizing telecom contact center operations with AI

>> Explore the case study: Boost productivity with AI-powered Smart Call Center Assistant

2.2. Agentic AI in logistics

The client is one of Vietnam’s leading providers of container solutions and logistics services. It operates a specialized fleet of refrigerated containers used to transport temperature-sensitive goods such as seafood, pharmaceuticals, and food products. Its biggest challenge was a manual monitoring process that was costly, labor-intensive, and often too slow to detect temperature fluctuations, creating a high risk of cargo spoilage.

TMA’s approach: TMA developed a comprehensive IoT-based Reefer Monitoring System:

• Real-time monitoring and control: The system continuously tracks temperature and humidity and displays the data on a centralized dashboard accessible via web and mobile applications. It also allows operators to adjust refrigeration settings remotely.

• Automated alerts: Notifications are sent immediately via SMS, Zalo, or Skype whenever monitored parameters exceed predefined thresholds, enabling staff to intervene promptly.

• Location tracking and data management: The system stores complete data logs for compliance reporting and tracks container locations through GPS-enabled digital maps.

Results:

• Optimized operations: Effective remote monitoring significantly reduced operating costs and minimized human error.

• Improved cargo protection: The system greatly reduced the risk of spoilage for temperature-sensitive goods throughout the transportation process.

• Stronger competitive positioning: The company can provide partners with transparent, reliable, and verifiable data on cargo conditions, strengthening trust and service credibility.

Case study: Monitoring refrigerated containers with AI and IoT

>> Explore the case study: AI Logistics Automation: Revolutionizing Supply Chains for Future Efficiency

2.3. Agentic AI in finance

Modern enterprises face increasing pressure to deliver immediate, high-quality customer support across multiple digital channels. However, relying entirely on human agents often results in slow response times, inconsistent answers, and higher operating costs, particularly during peak periods and outside regular business hours.

TMA’s approach: TMA developed an AI-powered Agentic Client Services Platform that automates customer responses, continuously learns from interactions, and integrates seamlessly with core business systems:

• Consistent omnichannel support: The platform receives and processes requests across web chat, email, social media, and messaging applications such as Zalo and Facebook Messenger.

• Natural, multilingual AI communication: Natural language processing enables the system to understand customer intent, communicate in a human-like manner, and provide real-time translation to overcome language barriers.

• Intelligent service optimization: The platform can automatically generate and update FAQs, monitor customer sentiment, recommend relevant products, and escalate complex cases to human agents together with the complete conversation history.

• CRM and ERP integration: Product data, order histories, and account information are synchronized to provide a 360-degree view of each customer.

Results:

• 24/7 availability: Customers receive immediate support at any time, including outside regular business hours.

• Scalable operations: The system can handle thousands of concurrent inquiries without requiring additional customer service staff.

• Consistent and accurate responses: Answers remain standardized and reliable across all communication channels.

• Reduced agent workload: Routine requests are automated, allowing employees to focus on complex, high-value cases.

• Higher customer satisfaction: Shorter wait times and faster, more personalized support improve the overall customer experience.

Case study: Automating customer service with Agentic AI

>> Explore the case study: Client Services Agentic Solution 

2.4. Agentic AI in healthcare 

Healthcare facilities frequently face overcrowding and long waiting times at reception areas. Manual registration processes are time-consuming and prone to errors, particularly during peak hours. The lack of an effective patient routing and queue management system also makes it difficult for hospitals to prioritize urgent cases.

TMA’s approach: TMA developed an AI-powered Smart Healthcare Registration Kiosk to fully digitalize the patient reception process:

• Self-service registration: Multilingual touchscreen kiosks allow patients to independently register for medical appointments and make hospital payments quickly and conveniently.

• Identity verification and data synchronization: The system combines facial recognition with chip-enabled citizen ID cards to verify patient identities. It also integrates seamlessly with Vietnam’s national health insurance system to retrieve data in real time.

• Intelligent patient routing: Acting as a smart reception point, the system performs initial triage based on the patient’s symptoms and preliminary assessment. The AI agent then automatically routes the case to the appropriate specialty department and assigns the corresponding queue number for consultation.

Results:

• Successful real-world deployment: The solution has been implemented at Hospital 199 and showcased at major healthcare events, including the Da Nang Medical Expo.

• Streamlined operations: It significantly reduces patient waiting times, removes process bottlenecks, and eases the administrative workload of front-desk healthcare staff.

• Improved safety: Advanced contactless technologies help minimize the risk of cross-infection and create a safer clinical environment.

• Enhanced patient experience: A seamless and well-organized registration process improves convenience and overall patient satisfaction.

Case study: Automating patient registration with AI

>> Explore the case study: Medical Registration Kiosk Modernizing Vietnamese Healthcare

Talk to our AI team →

3. How to choose an Agentic AI development partner in Vietnam 

To successfully deploy complex Agentic AI systems, enterprises should evaluate technology partners based on six core criteria:

• A proven production track record, not just PoCs or demos: Building a proof of concept or demo is relatively straightforward, but maintaining a stable AI system in a live production environment is far more challenging. Enterprises should therefore ask potential partners to provide examples of successfully deployed projects and review the measurable business outcomes those systems have delivered.

• Experience with agent orchestration, tool use, state management, and failure handling: Production-grade agentic systems require reliable orchestration across models, tools, data sources, and enterprise applications. For multi-agent use cases, enterprises should also assess how agents coordinate, share context, resolve conflicts, and recover from failures.

• Domain expertise in your industry, such as telecom, healthcare, finance, or manufacturing: AI cannot fully address business challenges if the development team lacks an understanding of industry-specific processes. Organizations should prioritize partners with relevant sector experience to reduce onboarding time and ensure that the project is aligned with business requirements from the outset.

• Security and data governance, particularly with Vietnam’s 2026 AI Law in effect: With Vietnam’s AI Law taking effect in March 2026, organizations must address requirements related to risk management, transparency, accountability, and AI system oversight. When selecting an Agentic AI development partner, enterprises should prioritize providers with clearly defined security and data governance practices, including access control, encryption, data management, audit logs, human oversight, and controls over AI agent activities.

• Post-deployment support for monitoring, evaluation, and continuous optimization: For Agentic AI systems, go-live is not the end of the project. After deployment, the partner should continue monitoring agent behavior in production and improving the system as new data becomes available. Enterprises should assess whether the provider supports performance monitoring and alerting, API and tool-call error tracking, user feedback collection, RAG and Post-deployment support for monitoring, evaluation, and continuous optimization. 

• Advanced data engineering and RAG capabilities: Poorly processed input data can cause AI systems to generate inaccurate or fabricated information, directly affecting business credibility. Enterprises should therefore evaluate the partner’s data engineering expertise and experience implementing retrieval-augmented generation to help round model responses in relevant and reliable enterprise data.

Evaluating an Agentic AI development partner’s capabilities

>> Explore more: Top AI agents development company for Your Business

4. Top agentic AI development companies in Vietnam

Over the next two to three years, Agentic AI is expected to move beyond the experimental stage and become directly integrated into ERP, CRM, and other core business processes. Gartner also predicts that by 2028, 33% of enterprise software applications will incorporate this technology. However, the success of these initiatives will depend heavily on system integration capabilities, security, and effective agent governance.

To support safe and effective implementation, below is a list of leading Agentic AI development companies in Vietnam that enterprises may consider:

4.1. TMA Solutions: Large-scale Agentic AI deployment with an AI Factory model

With more than 4,000 engineers and a dedicated AI Center backed by over 10 years of experience, TMA Solutions provides end-to-end Agentic AI transformation services for multinational enterprises.

• Agentic AI capabilities: The TMA AI Agent Development Factory supports the entire product lifecycle, from R&D and secure sandbox testing to packaging, deployment, and operational monitoring through AgentOps.

• Integration capabilities: TMA integrates AI agents deeply into Big Data platforms, cloud infrastructure, and core enterprise systems such as ERP and CRM, while maintaining stringent security and quality standards including ISO/IEC 27001 and ISO 9001.

• Best suited for: Large enterprises in telecommunications, logistics, healthcare, and manufacturing that require a partner capable of handling complex, large-scale projects.

4.2. FPT Software: Scaling Agentic AI with a low-code ecosystem

FPT Software combines a global engineering workforce, strong AI computing infrastructure, and international delivery experience to help large enterprises scale AI Agent adoption across their organizations.

• Agentic AI capabilities: Its AI ecosystem includes IvyHub, a low-code/no-code platform for creating and managing multi-agent workflows, and IvyChat, which supports voice agents and virtual assistants.

• Centralized governance: Business units can build agents for their own operational needs while remaining subject to centralized access control, governance, and risk management policies defined by the enterprise IT organization.

• Best suited for: Diversified corporations and large enterprises seeking rapid and scalable AI deployment.

4.3. Innotech Vietnam: Custom AI Agent development for banking and financial services

Innotech Vietnam approaches Agentic AI with a highly customized model tailored to each client’s IT architecture and operational processes.

• Agentic AI capabilities: The process starts with an AI Readiness Assessment, followed by the design of multi-agent workflows, API integration, and the implementation of security guardrails.

• Security and compliance: The solutions are optimized for highly regulated and security-sensitive environments. They support granular access control, encryption of sensitive data, and comprehensive audit logging.

• Best suited for: Banks, fintech companies, and insurance providers where data security and regulatory compliance are top priorities.

4.4. KMS Technology: Applying Agentic AI across the software development lifecycle

KMS Technology focuses on applying AI agents to real-world software development and operational challenges, rather than creating isolated proof-of-concept models with limited practical value.

• Agentic AI capabilities: KMS's Velox platform serves as a centralized environment that connects autonomous agents, development tools, and project data while providing end-to-end traceability.

• Control mechanisms: The platform incorporates a strong human-in-the-loop (HITL) approach, allowing AI agents to automate tasks from requirements analysis and code generation to automated testing, while remaining under close engineering supervision.

• Best suited for: Software development companies and technology enterprises seeking to improve IT productivity and optimize DevSecOps workflows.

4.5. NashTech: Integrating AI Agents into legacy infrastructure and complex workflows

NashTech stands out for combining AI architecture consulting, advanced software development, and legacy system modernization.

• Agentic AI capabilities: NashTech enables multiple AI agents to work together across complex, multi-step business processes without disrupting existing systems.

• Proven implementation: The company has delivered a multi-agent AI solution for United Trust Bank, where multiple agents collaborate to analyze complex loan applications while maintaining explainability and human auditability.

• Best suited for: Financial institutions, banks, and enterprises with long-established technology environments that want to adopt AI without replacing their entire existing infrastructure.

TMA Solutions’ AI Agent Development Capabilities

5. FAQs about Agentic AI development

1 - How is Agentic AI different from conventional Generative AI?

Generative AI primarily responds to prompts by creating content, while Agentic AI is designed to take a series of proactive actions to achieve a specific goal. The key difference lies in the system architecture and level of autonomy, rather than simply the AI model being used.

2 - How long does it take to build a production-ready Agentic AI system?

A system designed for a clearly defined use case typically takes around three to six months to become production-ready. This timeframe includes data integration, testing, security implementation, monitoring, and guardrail configuration. Multi-agent projects, integrations involving several enterprise systems, or deployments in highly regulated industries may take six to twelve months or longer.

The growing momentum behind Agentic AI development in Vietnam is creating significant opportunities for enterprises to automate core business processes at a deeper level. However, realizing meaningful value from this technology depends heavily on selecting a partner with both strong technical capabilities and a solid understanding of the business domain.

When you are ready to deploy AI agents safely and effectively in a real-world production environment, contact TMA Solutions to speak with our team of experts.

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