What is Agentic AI?

Artificial intelligence (AI) is evolving at a breakneck pace, moving far beyond its early capabilities. Initially, we had predictive AI, which analysed historical data to forecast future outcomes. This was followed by the rise of generative AI, a revolutionary technology capable of creating new content like text, images, and code based on user prompts. Now, we’ve arrived at the agentic AI stage — not only does AI generate content, but it’s also able to be conversational and autonomously act and react.

Agentic AI represents a pivotal shift from AI as a passive, instruction-following tool to AI as an active, autonomous partner. It empowers AI systems to not just respond, but to reason, plan, and act independently to achieve complex, multistep goals. Imagine autonomous agents that can act as digital labour, making decisions and adapting to new situations with remarkable efficiency.

For businesses in Singapore and across the dynamic ASEAN region‌ — ‌a digital economy poised to reach nearly US$1 trillion by 2030‌ — ‌understanding and harnessing agentic AI is no longer a future consideration. It's a present-day strategic necessity for driving innovation and maintaining a competitive edge.

What we'll cover:

• What are the key features or characteristics of agentic AI?
• What is the difference between generative AI and agentic AI?
• How does agentic AI work?
• What are some agentic AI use cases in the context of Southeast Asia?
• Benefits of Agentic AI
• What is an agentic AI platform?
• What are the challenges of agentic AI in Southeast Asia?
• The future of agentic AI in the region

Agentic AI is the technology that powers AI agents so they can act autonomously without human oversight. It facilitates seamless interaction between AI agents and humans, fostering a collaborative environment where both can work together. This platform has a suite of tools and services to help AI agents learn, adapt, and collaborate so they can quickly handle complex and dynamic tasks. It’s the next frontier of AI known for its ability to operate independently by making decisions, adapting to dynamic situations, setting goals, and reasoning.

One of the standout features of agentic AI is that it can simplify the development and deployment of AI agents, so integrating AI into daily operations is less resource-intensive. With this framework, businesses can customise AI agents to meet their specific needs, whether those needs involve automating repetitive tasks, enhancing customer service, or driving strategic decision-making.

Through natural language processing (NLP), agentic AI systems like Agentforce can mimic human behaviour, making them ideally suited to handle a wide range of intricate and ever-changing situations.

Their core capabilities rest on three key features:

1. Autonomy: Agentic AI can perform tasks and make decisions without requiring direct, step-by-step human instruction. It can initiate actions on its own to move towards a goal.
2. Adaptability: The system learns from its interactions and environment. It can receive feedback, understand what works and what doesn't, and modify its approach to improve its performance over time.
3. Goal orientation: You can provide an AI agent with a high-level objective, and it can reason, plan, and execute the sequence of tasks required to achieve it.

While related, generative AI and agentic AI serve fundamentally different purposes. Think of it as the difference between a brilliant writer and a highly effective project manager.

• Generative AI creates: Its primary function is to generate new content. When you give it a prompt, it uses its training data to create a specific output, such as an email draft, a marketing image, or a summary of a report. It excels at content creation but is reactive and waits for the next command.
• Agentic AI acts: Its primary function is to achieve a goal. It uses generative AI as its ‘brain’ to reason and plan, but its key differentiator is its ability to take autonomous actions. It can interact with different applications, execute multi-step workflows, and adapt its plan based on real-time results to complete a complex task from start to finish, with minimal human intervention.

In short, you tell generative AI what to create. You give agentic AI a goal, and it figures out how to achieve it.

Agentic AI operates through a complex network of autonomous software components known as ‘agents’ that draw from massive amounts of data and learn from user behaviour to improve over time. Each agent is designed with specific goals and abilities, working in harmony to tackle complex tasks. This innovative approach to agentic AI relies on a blend of advanced technologies such as machine learning, NLP, and knowledge representation that helps agents learn, communicate, and reason effectively.

The underlying architecture of agentic AI sits on a distributed systems platform, which ensures scalability and ‌high performance. This platform allows multiple agents to operate simultaneously across different servers to enhance the system's overall efficiency and reliability. Agents can collaborate in real-time by sharing information and coordinating their actions to more intuitively and seamlessly interact with users.

Agentic AI uses self-contained agents — AI models that autonomously complete tasks and manage workflows using machine learning, algorithms, and predictive analytics to make real-time decisions.

It follows a five-step process to solve complex problems:

1. Perceive: AI agents gather and decode information from sources like sensors, databases, and interfaces to turn data into insights. They pinpoint meaningful patterns and extract what’s most relevant in their environment.
2. Reason: A large language model (LLM) guides the reasoning process — understanding tasks, crafting solutions, and coordinating specialised models for jobs like content generation or image analysis.
3. Act: Agents perform tasks by connecting with external systems through APIs. Built-in guardrails ensure safety and compliance — like limiting insurance claim processing to specific amounts before human review.
4. Learn: Agents evolve through feedback and get better with every interaction to refine decisions and processes. This continuous improvement drives smarter performance and greater efficiency over time.
5. Collaborate: Agent building tools are designed to make it easy to deploy agents by lowering the barrier to entry for both experienced programmers and newcomers alike. The open-source nature of the agentic builder tools further encourages a dynamic community of contributors who continuously make the platform better. This collaborative effort not only accelerates the development of new features but also ensures that the technology remains at the forefront of AI innovation.

Think of a traditional AI like a chatbot that follows a script. Agentic AI, by contrast, is like an expert personal assistant.

It starts with a clear objective from a user‌ — ‌for example, "Find the most cost-effective shipping logistics from our factory in Vietnam to a distribution centre in Singapore for the next quarter."

The AI agent then autonomously breaks down this goal into smaller steps. It will:

1. Access internal inventory data to understand product volumes.
2. Scan real-time data from multiple shipping providers for pricing and schedules.
3. Analyse historical shipping data to predict potential delays.
4. Model different scenarios to balance cost against delivery time.
5. Present a fully reasoned recommendation with options.

This entire process happens without a human guiding each query, making it a powerful tool for navigating the complex ASEAN market.

Agentic AI isn't just a theoretical concept; it has practical applications that can transform core business functions.

• Customer service: An AI agent can manage complex customer issues from start to finish. It can understand a customer's problem, access their history, explore solutions, process a return, and schedule a follow-up, all while handling queries in multiple languages like English, Malay, and Mandarin to cater to the diverse regional audience.
• Sales: An agent can autonomously engage with inbound leads to answer questions, handle objections and book meetings for human sellers. It can also coach sales representatives on deals, and generate quotes in an instant.
• Marketing: Imagine an AI agent tasked with "increasing brand engagement in the Indonesian youth market." It could analyse social media trends, generate draft campaign content, deploy it across platforms, monitor results, and adjust the strategy in real-time based on performance.
• Risk management: In a hub like Singapore, an AI agent can continuously monitor real-time transaction data and market feeds to identify emerging credit risks or compliance issues. It can autonomously analyse behavioural anomalies that rule-based systems might miss and dynamically update a customer's risk profile, allowing compliance officers to act proactively.
• Fraud detection and prevention: Agentic AI can autonomously investigate suspicious transactions by cross-referencing data from multiple systems. If a high-risk activity is detected, an agent can temporarily freeze an account, initiate additional authentication measures, and escalate the case to a human analyst with a full summary, dramatically reducing response times and potential losses.

Adopting agentic AI offers transformative advantages for businesses, moving beyond simple automation to intelligent, proactive operations.

By automating complex, end-to-end workflows, agentic AI frees up human employees from time-consuming, repetitive tasks. This allows teams to focus on high-value strategic work, innovation, and creative problem-solving, directly boosting overall productivity.

Agentic AI can deliver truly personalised and responsive customer service at scale. By understanding individual customer history and context, agents can anticipate needs, offer tailored solutions, and provide 24/7 support, significantly improving customer satisfaction and loyalty.

Autonomous AI agents process vast data streams in real time, providing actionable insights for smarter decisions. By detecting patterns and forecasting outcomes, they enable faster, data-driven choices with confidence.

AI agents provide businesses with elasticity. They can scale operations up or down instantly in response to demand‌ — ‌like during peak shopping seasons‌ — ‌without a proportional increase in human resources. They can also adapt to disruptions by rerouting workflows, ensuring business continuity.

An agentic AI platform is the core technology that empowers a new generation of AI systems known as "AI agents." Instead of just executing a single command like a traditional chatbot, these agents can reason, plan, and autonomously perform multi-step tasks to achieve a goal.

For the diverse and dynamic ASEAN market, this platform approach is a game-changer, especially for small and medium-sized enterprises (SMEs) that often lack the resources to build complex AI from the ground up.

An agentic AI platform provides the foundational components needed to deploy these sophisticated agents, offering:

ASEAN isn't a single market, but a collection of diverse cultures, languages, and business practices. A platform allows a business in Vietnam to build an agent that understands local dialects and consumer behaviour, while a company in the Philippines can create one tailored to its specific customer service protocols. This adaptability is critical for delivering relevant and effective solutions across the region.

For an SME looking to grow, an agentic platform provides a scalable solution. A small e-commerce business in Thailand might start by using an agent to automate inventory updates and customer service inquiries. As the business expands, the platform can be used to create more advanced agents that manage supply chain logistics or analyse market trends, all without needing to hire an army of data scientists.

Without these platforms, the power of agentic AI would be limited to large corporations. By offering pre-built tools and services, a platform democratises this technology, making it accessible and affordable for a much wider range of businesses. This aligns with national strategies across the region, like Singapore's National AI Strategy 2.0 , which aims to accelerate enterprise adoption of AI to boost productivity and empower the workforce.

Adopting this powerful technology comes with unique regional challenges:

• Complexity and integration: Integrating AI agents with existing legacy systems across different markets can be technically challenging.
• Data governance: Navigating the diverse data privacy and sovereignty laws across ASEAN countries requires careful planning and robust security.
• Ethical considerations: Ensuring AI agents operate without bias and make fair decisions is critical. This requires building trust and transparency, especially in sensitive sectors like finance and healthcare.

Cost and talent: While platforms reduce the barrier to entry, there's still a significant need for skilled professionals who can manage and oversee these AI systems.

Agentic AI is set to become an indispensable part of the business toolkit. As ‌technology matures, we'll see agents that can collaborate with each other to solve even more complex problems, further accelerating digital transformation. For businesses across Southeast Asia, the time to explore and invest in agentic AI is now.

By embracing this technology, companies can unlock new levels of productivity, innovation, and customer value, securing their place as leaders in the world's most dynamic digital economy.