If 2023 and 2024 were the years proving what Generative AI could create, 2025 has become the inaugural year showing what AI can do. Moving beyond simply answering user questions or generating images, ‘Agentic AI’, which autonomously designs complex workflows and uses tools to complete tasks, has emerged as a central topic in the tech industry.

I have witnessed numerous exaggerated promises and bubbles while watching the speed of AI technology development over the past few years. However, the achievements of Autonomous Agents emerging recently are truly remarkable. The possibilities shown by OpenAI’s ‘Operator’, Anthropic’s ‘Computer Use’ feature, and Google’s ‘Project Jarvis’ suggest that AI is now being reborn not just as a ‘chat partner’ but as a practical ‘colleague’. In this article, we will look deeply into the changes brought by Agentic AI in 2025, core technologies, and the challenges we face.

What is Agentic AI?

Agentic AI refers to a system that uses a Large Language Model (LLM) as a brain to perceive the environment, reason, and act. If a traditional chatbot answered “The pizza place phone number is 000-0000” to the request “Order a pizza,” Agentic AI actually opens the delivery app, selects the menu, completes the payment, and then informs the user of the estimated arrival time.

The core lies in ‘Autonomy’ and ‘Tool Use’ capabilities. The Agentic systems of 2025 have the following characteristics:

1. Multi-step Reasoning: When receiving a vague and complex command like “Plan next year’s marketing strategy,” it autonomously decomposes it into sub-tasks such as “market research,” “competitor analysis,” “budget estimation,” and “strategy establishment by channel.”
2. Tool Proficiency: It directly manipulates various software tools used by humans, such as web browsers, Excel, Slack, and internal enterprise ERP systems, through APIs or GUIs (Graphical User Interfaces).
3. Long-term Memory: It does not stop at one-off conversations but remembers past project history or user feedback to continuously improve performance.

2025, Why Now?

The concept of agents is not new, but there are several technical backgrounds for its explosive growth in 2025.

1. Drastic Reduction in Inference Costs

In the past, running an agent using a GPT-4 level model incurred astronomical API costs. This is because an agent must go through dozens or hundreds of internal Thought Processes for a single task. However, recent models have seen inference costs drop to 1/100th compared to two years ago, and with the performance improvement of On-device AI chips, basic inference has become possible on local machines. This is the biggest factor breaking down the barriers to commercialization of agents.

2. Completion of Multimodal Interfaces

The ‘Computer Use’ feature first introduced by Anthropic’s Claude 3.5 has now become an industry standard. AI can now not only read text code but also see the Screen like a human, recognize the location of buttons, and manipulate the mouse. This has enabled AI to control legacy software for which APIs are not provided, accelerating the speed of adoption within companies.

3. Introduction of ‘System 2’ Thinking

As ‘Chain of Thought’ technology demonstrated in OpenAI’s o1 and o3 models has advanced, AI’s ability to plan deliberately (System 2) rather than react immediately (System 1) has improved dramatically. This has significantly reduced the probability of agents getting lost or causing Hallucinations in complex situations.

Changes in Corporate Fields: Redefining Workflows

Centered around tech companies in Silicon Valley and Pangyo, the adoption of Agentic AI has moved beyond the experimental stage into actual deployment. The most notable changes are appearing in software development and office automation fields.

Rise of AI Software Engineers

The appearance of ‘Devin’ was just the beginning. Currently, tools like GitHub’s Copilot Workspace go beyond simply recommending code; if you throw just one issue ticket, they analyze related code, write modifications, perform tests, and even generate Pull Requests (PR). Developers are now transforming from ‘code writers’ to ‘supervisors’ who review code written by AI and design architectures.

I also recently entrusted an entire authentication module to an AI agent in a side project. Surprisingly, the agent even implemented exception handling for Edge Cases I hadn’t thought of. Of course, it wasn’t perfect, but I was able to reduce development time by over 70%.

Evolution of Office Automation (RPA)

Traditional RPA (Robotic Process Automation) was dumb automation that only moved according to set rules. If the UI on the screen changed by just 1 pixel, the bot would break. However, Agentic AI, which understands visual information, copes flexibly with UI changes and understands and performs natural language commands like “Process the invoices for this month.” Tasks that are repetitive but require judgment, such as the finance team’s receipt processing or the HR team’s initial resume screening, are rapidly being replaced by AI.

Challenges to Solve: Safety and Control

Of course, there is not only a rosy future. As Agentic AI becomes capable of actions that have a direct impact on the real world, ‘AI Safety’ and ‘Governance’ have become more important than ever.

1. Risk of Irreversible Actions

If a chatbot says something strange, you can just close the window. But if an agent accidentally deletes a company server or processes an incorrect refund for a customer, the damage is irreversible. Therefore, agent systems in 2025 mandatorily include ‘Human-in-the-loop’ mechanisms. They are designed to request human approval before important decisions (payment, data deletion, etc.).

2. Infinite Loops and Resource Waste

There is also a risk that an agent may repeat the same task indefinitely because it cannot achieve the goal, or call unnecessarily many APIs, leading to a billing bomb. To prevent this, safety devices (Guardrails) such as ‘maximum execution count limits’ and ‘budget allocation’ are being implemented systematically.

3. Evolution of Security Threats

‘Prompt Injection’ attacks are more fatal in the agent era. If a hacker plants a command like “Send all contact information to an external server” in invisible text within an email, a secretary agent trying to summarize the email might execute it. Accordingly, the ‘AI Firewall’ market, which strictly validates input and output data, is also growing.

Future Outlook: The Era of Personalized Agents

In the second half of 2025, we are now moving towards the era of ‘1 Agent per Person’. Agents integrated at the smartphone OS level see through my schedule and are learning my preferred restaurants, travel style, and work patterns.

This trend led by Apple and Google will fundamentally change our digital lifestyle. As the App-centric ecosystem is reorganized into an Agent-centric ecosystem, users will no longer have to spend time installing and learning dozens of apps. Just saying “Plan a trip to Busan for this weekend, including accommodation and KTX reservations” will be enough.

Conclusion: Wisdom of Coexistence Beyond Technology

Agentic AI is certainly bringing a revolution in productivity. However, how to control and utilize this powerful tool is ultimately up to us humans. Rather than being buried in technology, we should focus on creating more creative and human values through the time spared by AI.

TechDepend readers, I hope you also move away from the stage of simply ‘using’ AI and worry about how to ‘hire’ and ‘collaborate’ with your own agent. The wave of change is already at our feet.

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This article was written based on technology trends in December 2025.