The Era of AI Agents: A Comprehensive Guide from Development to Cost Management

AI Agents: A New Driving Force Shaping the Future

Artificial Intelligence (AI) technology is fundamentally transforming our lives and work. In recent years, interest in AI agents has exploded, bringing the potential of AI to solve complex problems and make autonomous decisions, beyond simple repetitive task automation, closer to reality. At the heart of this transformation are ‘AI agents.’ An AI agent is intelligent software that plans, acts, and interacts with its environment to achieve specific goals. It is evolving into an entity capable of understanding context and actively solving problems, a significant leap beyond traditional chatbots or voice assistants.

In this article, we will explore the current trends in AI agent technology and provide an in-depth analysis from a practical perspective, covering everything from development to real-world application and cost management. Specifically, we will offer practical information reflecting the latest IT trends, including Google ADK for developers, key considerations when building agent systems, and the importance of GPU cost management. Through this, readers will be able to successfully embrace the era of AI agents and gain insights for business innovation. AI agents are more than just a technological trend; they are becoming a core driving force that will redefine our future. How should we prepare for this monumental shift?

The Rise of AI Agent Technology

The rapid advancement of AI agent technology is the result of a combination of several factors. The most significant driving force is undoubtedly the remarkable progress of Large Language Models (LLMs). LLMs like GPT-3, GPT-4, and Gemini demonstrate human-like language understanding and generation capabilities, providing the foundation for AI agents to comprehend complex commands, communicate naturally, and perform tasks.

Furthermore, the development of cloud computing has improved accessibility to the vast computational resources required for AI model training and inference. It has become possible to run large-scale AI models and build environments where numerous agents operate simultaneously, something unimaginable in the past. This technological progress is accelerating the movement of businesses to apply AI agents beyond mere research subjects to actual business operations.

The Evolution of AI Agents: Beyond Simple Automation

Previous automation tools were limited to handling simple, repetitive tasks according to predefined rules. AI agents, however, are different. They learn autonomously, analyze situations, and formulate optimal action plans to achieve given goals. For instance, an AI agent responding to customer inquiries can go beyond providing pre-prepared answers; it can understand the customer’s emotional state and context to offer personalized responses. Moreover, multiple agents can collaborate to execute complex projects or adjust investment strategies in real-time to adapt to changing market conditions, performing high-level tasks.

The advancement of AI agents is expected to bring revolutionary changes across various industries. In finance, they can provide personalized investment advice and asset management services; in healthcare, they can assist with disease diagnosis and accelerate drug discovery. Furthermore, in manufacturing, they can maximize efficiency through production process optimization and predictive maintenance, while in the entertainment industry, they can recommend content tailored to individual tastes or offer new forms of interactive experiences.

AI Agent Development: Google ADK and Latest Trends

As the potential of AI agents becomes a reality, the importance of tools and frameworks for their effective development and deployment is growing. Google’s Agent Development Kit (ADK) is a powerful framework that helps developers build AI agents more easily and efficiently. ADK maximizes development flexibility by supporting various agent types, multi-agent architectures, and multiple programming languages. This can be seen as an attempt to systematize the AI agent development process, much like traditional software development. Developers can use ADK to simplify the design, deployment, and management of complex agent architectures. Notably, Google recommends deploying ADK agents on its Vertex AI Agent Engine runtime, which demonstrates Google’s strategy to provide an integrated solution for cloud-based AI agent management and scaling. Another significant advantage is the ability to flexibly deploy agents in various environments, including Google Cloud Run, using containerization technology. This reduces vendor lock-in and allows developers to operate agents in their preferred environments. ADK supports multiple languages such as Python, Go, Java, and TypeScript, facilitating integration with existing development ecosystems. This means developers can participate in AI agent development using languages and tools they are already familiar with. This approach will lower the entry barrier for AI agent development and lay the groundwork for more innovative ideas to be realized. Currently, ADK, along with competing frameworks like Amazon Bedrock Agent Core, Azure AI Foundry Agent, and Databricks Agent Bricks, is shaping the AI agent development ecosystem. This competitive landscape will be a crucial factor driving the rapid advancement of AI agent technology.

Key Features and Advantages of ADK

The greatest advantages of Google ADK are its ‘modularity’ and ‘flexibility.’ Developers can design agent architectures tailored to their needs by combining various components provided by ADK. For example, they can selectively integrate sensors, actuators, or learning modules required for specific tasks. ADK also facilitates the construction of multi-agent systems, enabling scenarios where multiple agents collaborate and communicate to solve complex problems. This is similar to multiple experts forming a team to carry out a project. In terms of language support, ADK supports Python, Go, Java, and TypeScript, expanding developers’ choices. This makes integration with existing systems or libraries easier and is advantageous for collaboration among developers with diverse technical stacks within a team. Such multilingual support is a crucial factor in increasing the accessibility of AI agent development. Furthermore, while ADK is optimized for Gemini and the Google ecosystem, it aims for ‘openness,’ not being confined to specific models or deployment environments. This means developers can experiment with various LLM models and deploy agents in their preferred cloud or on-premise environments. This openness reduces dependence on specific technologies and contributes to accelerating the pace of technological innovation. Finally, ADK operates as an open-source project, continuously evolving through community participation and contributions. This positively impacts various aspects, including bug fixes, feature enhancements, and the addition of new libraries. Developers can participate in the ADK community to gain the latest information, share their experiences, and collectively advance the technology.

Comparison with Other Frameworks

The AI agent development market is growing rapidly, with various frameworks competing. Key frameworks directly competing with Google ADK include Amazon Bedrock Agent Core, Microsoft Azure AI Foundry Agent, and Databricks Agent Bricks. Amazon Bedrock Agent Core leverages AWS’s robust cloud infrastructure and offers the advantage of utilizing Amazon’s diverse AI services. Azure AI Foundry Agent’s strength lies in its integration with Microsoft’s Azure cloud platform, optimizing it for building and managing AI agents in enterprise environments. Databricks Agent Bricks utilizes the capabilities of the Databricks data analytics and machine learning platform, providing specialized features for data-centric AI agent development. Additionally, frameworks such as OpenAI Agent SDK, LangChain/LangGraph, CrewAI, and SmithOS are enriching the AI agent development ecosystem. LangChain is a popular open-source framework for LLM-based application development, supporting various scenarios through modular components and flexible chain configurations. CrewAI is designed for multiple AI agents to collaborate on complex tasks, excelling in building teamwork-based agent systems. Each framework possesses unique strengths and characteristics, and developers must consider project requirements, the desired cloud environment, and the team’s technical stack to select the most suitable framework. For instance, if primarily using the AWS ecosystem, Bedrock Agent Core might be advantageous; if building complex data analytics pipelines, Databricks Agent Bricks could be more suitable. ADK secures its competitiveness based on its interoperability with Google Cloud, diverse language support, and the advantages of an open-source ecosystem.

Practical Application: Strategies for Building Successful AI Agent Systems

While AI agent technology is advancing rapidly, building successful systems in real-world business environments is far from easy. Many companies encounter unexpected difficulties in AI adoption projects or fail to achieve their expected ROI (Return on Investment). Successful AI agent system implementation requires a comprehensive approach that includes not only technical aspects but also organizational culture, processes, and ethical considerations. In particular, ‘agentic systems,’ which aim for AI with autonomy beyond simple automation, demand more meticulous design and management. The key is to manage the unpredictability that can arise as agents learn and make decisions independently, and to guide them in a direction consistent with business objectives. Currently, many organizations struggle to demonstrate measurable results from AI agent experiments, and the trust in AI’s decision-making is also low. This reality starkly illustrates the complexity and challenges of AI agent adoption. Therefore, for successful implementation, clear goal setting, a phased approach, and continuous evaluation and improvement are essential. It’s crucial to go beyond merely adopting the latest technology and ensure that the technology creates business value.

Considerations for Agentic System Design

When building an agentic AI system, which is an AI agent system with autonomy, several key considerations are essential. First, the agent’s components such as ‘runtime, brain, hands, memory, and guardrails’ must be clearly defined. Runtime refers to the environment where the agent operates; the brain is the AI model responsible for decision-making; hands are the interfaces for interacting with the external world; memory is the space for storing past experiences and data; and guardrails are control mechanisms that limit the agent’s scope of action and ensure safety. Systematically designing these components is the first step towards enhancing the agent’s stability and efficiency. Second, pitfalls in agent development, such as ‘nondeterminism’ and ‘token bloat,’ must be avoided. AI models can inherently generate unpredictable responses, and LLMs may attempt to use too much information during a conversation. To mitigate these issues, appropriate prompt engineering, model tuning, and efficient memory management strategies are necessary. Third, preparing for ‘agentic misalignment’ is crucial. This refers to LLM-based agents generating false information or behaving in unexpected ways to achieve their goals. To address these security gaps, continuous monitoring, reinforcement learning-based safeguards, and adherence to ethical guidelines are essential. These problems can become even more complex when agents need to integrate with various systems and perform multi-stage workflows. Therefore, it is important to identify potential risks from the initial design phase and develop strategies to mitigate them. By carefully designing and building the system with these factors in mind, one can minimize problems in a complex agentic AI environment and achieve successful outcomes.

Pilot Project Failures and Success Factors

Many organizations conduct pilot projects for AI agent system adoption, but over half fail to achieve measurable results. The causes of these failures are diverse, but common factors include ‘lack of clear objectives,’ ‘setting overly ambitious expectations,’ ‘insufficient understanding of technical limitations,’ and ‘inadequate data preparation.’ For example, treating a pilot project merely as an opportunity to test the latest technology, or expecting enterprise-level system performance from the pilot phase, increases the likelihood of failure. For a successful pilot project, the following strategies are necessary. First, ‘measurable, specific goals’ must be set. For instance, define clear and quantifiable objectives such as ‘reduce processing time for a specific task by 10%’ or ‘improve customer satisfaction by 5%’, and judge the success of the pilot project based on the achievement of these goals. Second, ‘small, repeatable experiments’ should be conducted. Rather than attempting to build a massive system from the outset, it is safer to start small, focusing on specific functions or workflows, and gradually expand based on pilot results. Third, ‘realistic expectations’ must be set. AI agent technology is still evolving, and expecting perfect performance is difficult. The pilot phase should focus on exploring the technology’s potential, identifying potential problems, and seeking directions for improvement. Fourth, ‘close collaboration with domain experts’ is essential. Not only the technical development team but also frontline staff who perform the actual tasks must actively participate in the project. Frontline staff, with their deep understanding of actual work processes and requirements, can significantly contribute to correctly setting the direction of the pilot project and enhancing the effectiveness of technology application. Finally, ‘learning from successful case studies’ is also important. Analyzing successful AI agent adoption cases from other organizations and applying their strategies and lessons learned to one’s own situation can help reduce trial and error. By comprehensively considering these factors, the probability of pilot project success can be increased, ultimately leading to the establishment of a successful AI agent system.

AI Cost Management: The Importance of GPU Cost Reduction

The proliferation of AI technology offers innovative opportunities for businesses but simultaneously imposes significant cost burdens. Particularly, the cost of GPUs (Graphics Processing Units), essential for AI model training and inference, is a major obstacle for companies. According to data published by Datadog, GPU instance costs account for a substantial portion of a company’s total computing expenses, making the development of AI-based technologies even more challenging. Many companies are aware of the increasing trend in AI costs but struggle with allocating GPU costs by business unit, understanding workload contexts, and devising cost-efficient improvement strategies. This situation complicates systematic budgeting and planning. If GPU cost management is not properly implemented, it can not only worsen the ROI of AI projects but also negatively impact the company’s overall financial health. Therefore, businesses must focus on developing strategies to effectively manage and optimize GPU costs during the expansion of AI infrastructure. This is not merely about cost reduction but a critical task for the sustainable growth and competitiveness of AI technology. Solutions like GPU monitoring can play a vital role in addressing these issues. Through such solutions, companies can monitor GPU usage in real-time, identify bottlenecks, and implement cost-effective improvements.

Causes and Impact of Rising GPU Costs

The explosive growth of AI workloads is driving a surge in GPU demand, leading to increased GPU hardware prices and higher costs for cloud GPU services. Deep learning models require high-performance GPUs to process vast amounts of data in parallel. Modern AI models, in particular, demand significantly more computing power than previous generations, necessitating greater GPU resource allocation. This increased demand exacerbates GPU supply shortages and is a primary driver of price increases. Furthermore, substantial GPU resources are consumed not only during AI model training but also during the inference process. AI services that must respond to user requests in real-time and perform complex calculations require continuous GPU resource input, further escalating GPU cost burdens. Rising GPU costs directly impact corporate AI investment decisions. If the economic viability of AI projects deteriorates, it can hinder the adoption of new AI technologies or the expansion of existing AI services. For startups and small and medium-sized enterprises, in particular, exorbitant GPU costs can be a significant barrier to entry into the AI market. This raises concerns about creating an imbalance in AI technology development and impeding the pace of innovation. Ultimately, increasing GPU costs can prevent businesses from fully leveraging the potential benefits that AI offers. Therefore, GPU cost management is a crucial task for the democratization and sustainable development of AI technology.

Datadog GPU Monitoring Solution

Datadog has officially launched its ‘GPU Monitoring’ solution to help companies overcome these GPU cost-related challenges. This solution provides integrated visibility across the entire AI stack, enabling businesses to systematically manage the health, cost, and performance of their GPU fleet. Datadog GPU Monitoring goes beyond simply tracking GPU usage; it directly links GPU usage to actual business teams and workloads. This allows for a clear understanding of how much GPU resources development teams, data science teams, or specific project teams are consuming, and what the associated costs are. This visibility plays a crucial role in identifying inefficient GPU usage patterns and quickly pinpointing workloads causing bottlenecks. Furthermore, the Datadog solution helps in devising concrete action plans to optimize GPU costs. For instance, companies can consider scheduling GPU tasks during off-peak hours or migrating to more efficient GPU instances. Jan B. Li, Datadog CPO, noted, “When resource inefficiencies, training and inference workload delays, and cost increases occur, AI cost management becomes a core agenda for executives, but most companies are still in the solution testing phase and lack a single view of the overall stack.” Datadog’s GPU Monitoring addresses these issues, serving as a powerful tool that helps companies manage AI costs more effectively and maximize the ROI of their AI investments. This solution is a culmination of Datadog’s technological expertise as a specialist in AI-powered observability and security platforms.

AI Agent Use Cases and Precautions

AI agent technology is already generating substantial value across various sectors. Criteo’s proposed ‘Commerce Outcome’ strategy is a prime example of maximizing marketing performance by combining agentic AI with real-time commerce data. This strategy is concretized around an ‘agentic recommendation service’ that suggests products with high purchase conversion potential in real-time, through LLM integration, MCP infrastructure, and collaboration with generative AI companies. Its goal is to understand consumers’ unpredictable journeys in fragmented multi-channel environments and automate optimized marketing activities at each stage to achieve tangible business results. This demonstrates that AI agents can play an active role in achieving business objectives, beyond merely providing information. Furthermore, Valve’s Steam Deck provides game developers with real-world player performance data to aid game optimization. This is a good example of how AI-driven data analysis can be used to understand the complex relationship between hardware performance and software optimization and improve user experience. Developers can analyze average FPS, frame rate trends, and more to identify game performance bottlenecks and measure performance changes before and after updates, thereby enhancing game quality. These cases suggest that AI agents have the potential to drive innovation across diverse industries.

Real-World Use Cases: Commerce and Gaming Industries

In the commerce sector, AI agents are revolutionizing customer experience and contributing to increased sales. Criteo’s ‘agentic recommendation service’ analyzes users’ real-time behavioral data and purchase history to accurately recommend the most suitable products to individuals. This allows customers to find desired products more easily, while businesses can increase conversion rates. Additionally, AI agents can automate personalized marketing campaigns, provide real-time responses to customer inquiries, and be utilized for inventory management and logistics optimization. These AI-powered solutions play an essential role in understanding complex and unpredictable consumer behavior patterns and responding effectively to them. ‘Commerce intelligence’ platforms leverage such AI technology to help businesses make data-driven decisions and respond agilely to rapidly changing market conditions. AI shortens the time marketing professionals spend analyzing vast amounts of data and extracting key insights, ultimately contributing to improved business performance.

In the gaming industry, AI agents also play a crucial role. Valve’s Steam Deck provides game developers with average FPS (frames per second) data collected from actual play environments. This allows developers to understand how games perform across various hardware configurations, diagnose performance degradation issues, and carry out optimization work. For example, if game performance drops sharply with a specific hardware combination, developers can use this data to create a patch to resolve the issue. Such optimization, based on real user data, critically impacts improving game quality and enhancing the player experience. The Steam Deck Verified program supports developers in effectively utilizing this data and will be expanded to include games that receive the ‘Steam Deck Playable’ rating in the future. This demonstrates how AI-driven data analysis can provide significant insights throughout the game development process and contribute to increased player satisfaction.

Potential Risks and Failure Cases

While building AI agent systems offers clear advantages, there are also undeniable potential risks and possibilities of failure. One of the biggest issues is ‘agentic misalignment.’ This refers to situations where an AI agent behaves in unexpected ways or even generates incorrect or false information while attempting to achieve its assigned goals. For example, an investment advisory AI agent might recommend overly risky investments to achieve high returns, or a legal advisory AI agent might provide inaccurate legal information. Such agentic misalignment can severely undermine trust in AI systems and lead to serious financial and legal problems. Another major issue is ‘cost increase.’ High-performance GPU resources, in particular, are essential for AI model training and operation, but their costs are very high. As AI agent usage grows, GPU consumption also increases, which can lead to unexpected rises in operating costs. The issue related to Claude code in Anthropic’s Claude Pro and Max pricing plans starkly illustrates this cost problem. The emergence of agentic AI features rendered existing flat-rate pricing models structurally unsuitable, prompting the need for pricing plan readjustment and cost management. Finally, ‘data privacy and security issues’ are also significant risks. AI agents often need access to sensitive personal information or corporate confidential data during their learning and operation. If such data is improperly handled or leaked, it can lead to severe privacy breaches and security incidents. Therefore, when designing and operating AI agent systems, it is crucial to establish strict data security policies and rigorously comply with relevant regulations. Recognizing these risk factors in advance and preparing appropriate countermeasures is key to building a successful AI agent system.

When adopting AI agents, potential risks such as the generation of incorrect information due to ‘agentic misalignment,’ increased operational burden from ‘rising GPU costs,’ and exposure to ‘data privacy and security’ vulnerabilities must be thoroughly managed.

Future Outlook for AI Agents

AI agent technology is expected to advance further, profoundly impacting our society and industries. The ‘multi-agent system’ currently under discussion represents a more advanced form of AI where individual agents collaborate and compete to solve complex problems. Such systems are anticipated to generate synergistic effects that surpass the capabilities of individual agents, driving innovation in various fields such as scientific research, complex simulations, and social system optimization. For example, multiple AI agents could collaborate to accelerate drug discovery or enhance the accuracy of climate change modeling. Furthermore, a setup where each agent specializes in a particular domain, exchanging information to find optimal solutions, will play a crucial role in overcoming the limitations of current single AI models. The development of these multi-agent systems will strengthen AI’s role as an intelligent partner that collaborates and interacts with humans, moving beyond being a mere tool.

The Potential of Multi-Agent Systems

Multi-agent systems aim for multiple AI agents, each with their own roles and objectives, to interact and achieve a common goal. This is similar to diverse specialists forming a team to execute a complex project. Each agent possesses capabilities specialized for specific tasks, and they share information, negotiate, and sometimes even compete with other agents. This collaborative model is highly effective for solving complex, multi-layered problems that individual agents alone cannot address. For example, in supply chain management, each agent can be responsible for a specific area such as production, logistics, or inventory management, sharing real-time data to find optimal supply chain operation strategies. Scenarios are also possible in financial markets where multiple investment agents exchange information and interact in real-time based on their respective analytical models and strategies to respond to market volatility. These multi-agent systems also offer the advantages of flexibility and scalability. It is easy to improve the overall system performance or add new functionalities by adding new agents or modifying existing ones. This can be a very attractive solution for modern businesses that need to respond agilely to constantly changing business environments. Ultimately, multi-agent systems have the potential to dramatically expand the scope of AI technology application and pioneer innovative use cases that were previously impossible.

AI Agents and Sustainable Technological Development

The advancement of AI agent technology holds the potential to enhance efficiency and productivity across society, but it also necessitates considerations for sustainable development. Amazon’s $5 billion investment in Anthropic highlights the critical importance of vast computing resources required for AI development. Anthropic’s move to secure long-term compute capacity through AWS’s Trainium chips suggests a deepening concern about how to efficiently acquire and utilize such resources alongside improving AI model performance. This demonstrates that the evolution of AI technology is not solely limited to algorithmic improvements but is also closely linked to building sustainable infrastructure, including the development of energy-efficient hardware and the construction of eco-friendly data centers. Furthermore, deep reflection on the ethical and social impacts of AI agents on society is essential. Issues such as AI bias, job displacement, and personal data protection are critical challenges that must be addressed alongside technological progress. Therefore, the future of AI agent technology depends not merely on creating more powerful and intelligent AI but on pursuing a balanced approach that ensures technology positively impacts humanity and society. This requires the efforts of technology developers, policymakers, and all members of society, who must continuously strive for AI technology to develop in an ethical and responsible manner. Ultimately, the sustainable development of AI agent technology hinges not only on technological innovation itself but also on carefully considering and managing its societal impact.