Since 2024, "Agentic AI" has rapidly gained popularity. It is defined as the next generation of agents that can autonomously perceive their environment, set goals, invoke tools, execute tasks, and self-correct when necessary. Compared to traditional RPA (Robotic Process Automation) or virtual assistants, Agentic AI's slogan is more resonant: no longer just executing "mechanical scripts" but possessing "agency like a human." Thus, 2025 has been referred to by some media and investors as the "Year of AI Agents." Tech giants are launching projects, and the capital market is flocking to it. However, alongside the hype comes increasing skepticism: is the actual capability and scene adaptability of current Agentic AI really as industry promotional materials claim? Especially since Gartner predicts that by the end of 2027, more than 40% of Agentic AI projects will be canceled due to rising costs, unclear commercial value, or insufficient risk control! Are we witnessing an AI agency revolution, or are we falling into another round of conceptual capitalization traps?

Image source: Internet

What is Agentic AI?

By definition, Agentic AI refers to AI agents that use machine learning models and connect various services and applications to automatically execute tasks or business processes. It can be understood as a mechanism where AI models respond to inputs continually through an iterative feedback loop, benefiting from applications and API services. The core concept is that given a task, such as: "Help me find all information related to AI agents, summarize it, and send it to my email," an AI model that is authorized, able to read web pages and the email client interface, and access email data should be able to comprehend and execute this natural language instruction more efficiently than program scripts or human employees.

Many people often confuse Agentic AI with AI Agents, and the boundary between these two terms remains blurry. The term "Agentic AI" does not yet have a clear owner, and once a few companies begin to take the lead in this field and create truly impressive products, its meaning and boundaries will become clearer.

Before that, let me share my understanding:

AI Agent typically means that a robot or tool can complete tasks more intelligently than traditional automation (RPA). It can read, reason, and act, but it still follows strict rules. Agentic AI covers a broader scope; it is a system in which many agents work together, and more importantly, it can make its own decisions based on the environment and create new paths when necessary, not merely following the rules set by humans but learning and adapting according to rules and the environment.

However, whether AI Agent or Agentic AI, the distinction from large models is very clear, below is a comparison table of large models vs. Agentic AI that can help you grasp the differences more intuitively:

What Role Does Agentic AI Play?

In the history of technology, there are countless instances of "concepts first, profits first": the 1990s internet bubble, the 2017 blockchain white paper wave, metaverse hype... many companies do indeed take a well-known term to "harvest the believers' money." This is not because the technology itself is a scam, but because business and capital often sprint ahead of actual implementable applications.

Therefore, we must distinguish between two things:

What can Agentic AI do as a technological direction?

The common market statements are very superficial: Agentic AI is "AI that can help you with tasks," but if it stops at "automatically replying, running workflows," then it is not much different from traditional automation (RPA, scripts, CRM plugins). The depth of Agentic AI actually lies in the philosophical and engineering challenge it attempts to solve: how AI can change from being a "passive tool" to an "active collaborator."

1. What shortcoming does "Agentic AI" actually fill?

The current large models are powerful, but they are passive: you ask questions, they answer. The problem is that: they lack the ability to be goal-driven; they do not have environmental awareness and feedback loops; they lack long-term memory, only able to "sprint" within the context window.

Thus, the essence of Agentic AI is: adding a goal-setting mechanism for AI (for example, "help me improve conversion rates, not just write an ad copy"); giving it tool usage and environmental interaction capabilities (able to call APIs, execute actions in systems); and giving it status tracking and memory (capable of continuously adjusting strategies over long task durations).

This transforms it from a "universal answer machine" to a "semi-autonomous entity."

2. The Scientific and Engineering Challenges of Agentic AI

It is not a single technology but a combination of several disciplines. First, there is the cognitive science aspect: how to have AI simulate the "goal-plan-execute-reflect" loop without "self-awareness"? Secondly, the software engineering aspect: how to seamlessly integrate LLMs with existing enterprise software (ERP, CRM, databases)?

Finally, from a control theory perspective: how to ensure that a "self-directed agent" does not go out of control but is always constrained within boundaries? For analogy: it’s like before building "self-driving cars," you need to solve three big problems—how does the car understand road conditions (perception), how does it plan routes (decision-making), and how does it brake safely (constraints). These are the challenges Agentic AI faces.

3. Why do we not see significant changes now?

Because the path for Agentic AI to take shape must be **gradual**. I humbly believe that possibly Phase 1: Agentic AI = "smarter plugins" (like customer service agents, automated e-commerce operations agents); Phase 2: Agentic AI = "workflow schedulers" (able to switch seamlessly between multiple systems, like helping a market team complete research—write reports—send emails chain); Phase 3: Agentic AI = "semi-independent digital employees," able to self-decompose tasks in the case of vague goals and continue executing. Most of what we currently see is still in the first and second phases, hence the disparity between the hype and the reality.

In other words: the value of Agentic AI lies in reducing the “switching costs” and “mechanical labor” of humans in complex tasks.

Image source: Internet

Practical Implementation of Agentic AI in Customer Service Scenarios

Users who have seen my previous content know that I am an AI customer service agent. Let me analyze the customer service scenario. Compared to the sci-fi imagination of a “universal digital secretary,” AI customer service agents have already been practically implemented in many industries. Their value is straightforward: saving manpower, saving time, and improving customer satisfaction.

Function Summary

Automatic replies to frequently asked questions: In e-commerce, banking, telecommunications, and subscription services, 70–80% of customer inquiries are actually repetitive and standardized: "What is your return policy?" "How do I check my bill?" "How do I renew my membership?" AI customer service agents can automatically identify these questions and respond within seconds without human intervention. Compared to traditional FAQ robots, the difference is that it can understand colloquial inquiries. For example, if a user says, "Can I return these shoes for a larger size?" It can correspond to "return and exchange policy."

Unified multi-channel service: In the past, customer service had to open multiple backgrounds: online service on the website, WeChat, WhatsApp, email, and phone records… The AI agent can provide a "unified entry for all channels", meaning no matter which platform the customer asks on, the agent can catch and manage it uniformly, and remember the context. For example: If a user inquired about logistics on WeChat yesterday and today inquired on the official website about “why hasn’t it arrived yet,” the agent can automatically retrieve the previous dialogue and continue answering instead of saying, "Please describe again."

Process-driven task execution: The agent not only answers but can perform "workflow actions." For instance, if a user says, "I want to return the product," the agent not only sends an explanation but also automatically: verifies the order number; generates a return order; sends the logistics tracking number or a link for pick-up. This evolves from a "reply robot" to a capable digital employee.

Cross-language instant customer service: Common in cross-border e-commerce, travel, and gaming industries. For example, if a customer asks a question in Spanish, the agent understands it in English/Chinese, automatically translates it, generates a reply, and then sends it back in Spanish. This reduces the complex issues that multi-language customer service teams have to deal with significantly, directly reducing costs.

Detailed Explanation of Automatic Replies

One of the core functions of my service can be summarized in four words: "automatic reply." Automatic replies are not solely for "customer service tickets" but are a full-scenario technology for "dialogue touchpoints." If traditional customer service is "customers ask, I answer," then AI automatic replies have now developed into "anywhere there’s a dialogue, we can insert ourselves." I will unfold it in several scenarios:

Private domain replies (WeChat/WhatsApp/Enterprise WeChat): Users in private domain communities ask: "When does this event end?" The agent can reply instantly and attach the registration link. Furthermore: The agent can also recognize "intention customers." For example, if someone says, "How much is this product?" The system will tag it as 【high potential lead】 to remind sales to follow up. 👉 Benefit: Reduces the minor burdens of community operation and allows human resources to focus on closing deals.

Social media replies (Facebook, Instagram, TikTok comments/direct messages): After ad placements, many people in the comment section will ask: "Is there stock?" "Can it be delivered to Malaysia?" AI can identify comments/direct messages and reply automatically, avoiding missed peak interest. If there are aggressive comments, it can use preset phrases to "softly neutralize," avoiding damage to brand image. 👉 Benefit: Seizing timely opportunities improves conversion rates in brand exposure.

Lead reception after ad placement: Users click on ads and leave phone numbers/emails/forms. AI immediately “greets” them, for instance, emailing: "Hello, I am an advisor from X brand; here’s a product brochure."; WhatsApp: "Hi~ I see you’re interested in our services; can I help you schedule a consultant?" Through several rounds of automatic dialogue, "cold leads" are turned into "hot leads." 👉 Benefit: Shortens response time (Lead Response Time), increasing conversion rates. (Many studies show that leads responded to within 5 minutes have conversion rates over 10 times higher than those responded to after an hour.)

Automatic pre-sales/after-sales replies on e-commerce platforms: **Before sales: Users ask, "Are there any discounts?" "How long is the shipping?" After sales: Users ask, "How do I track my shipment?" "Can I exchange sizing?" AI can directly bind to ERP/logistics interfaces to achieve real-time answers. 👉 Benefit: Reduces negative feedback while increasing repurchase rates.

Internal team support (enterprise internal agents): Employees ask in Slack/Feishu, "Where is this document?" AI replies directly and pulls up the policy document. If it’s IT repair, AI can help with basic troubleshooting before deciding whether to submit a work order. 👉 Benefit: Reduces the repetitive Q&A pressure on HR/IT.

Image source: Internet

What’s Real and What’s Hype in Commercial Promotion for Agentic AI?

Many businesses under the banner of "Agentic AI changing the world" actually want to:

• Sell API usage quotas (computational consumption is money);

• Sell SaaS subscriptions (packaging a "smart assistant" for a monthly fee of several dozen dollars);

• Attract investment and valuation (using Agentic as a story to project 100-fold future growth).

This is like companies presenting "everyone wears helmets to work" visions in earlier metaverse conferences, while the actual product was just a basic version of VR social software.

Therefore, people who believe in technology should not waste money in vain. The key is to recognize:

• The difference between 'usable' and 'conceptual': actual functionality on the ground (like automatic customer service replies) is valuable, while only discussing visions without implementation (like "super AI CEO") is deceiving.

• Return on Investment: If an agent tool can help you save dozens of hours each month, is it worth a subscription fee? This is a solid comparison.

• Looking at the maturity of the ecosystem: Once multiple companies are using it and standardized interfaces emerge, it often indicates it is approaching implementation; if it’s just one company boasting, then caution is warranted.

Conclusion

Although people hold high expectations for its ability to automatically execute tasks without constant human oversight, in reality, whether it’s the experimental results from Carnegie Mellon University’s “TheAgentCompany” or the evaluation reports from Salesforce’s research team on customer relationship management (CRM) scenarios, they reveal a fact: although the potential of Agentic AI is promising, it still faces a significant gap from effectively replacing human workers in real office scenarios.

Current Agentic AI seems more like it is in the valley before the "expectation plateau" in the technology hype cycle. Now, everyone discusses more and uses less; it seemingly feels "empty," but in fact, there are people laying the groundwork. Gartner’s technology curve theory states that: a new technology usually needs to experience—introduction → excessive hype → bubble → stable application → mature adoption.

In other words: not fulfilling sci-fi promises does not mean it's fake; rather, it’s still going through the long process of "from flashy demos to something ordinary people can’t do without." Just like the internet in the 1990s; at first, people wondered, "What can the internet do? Chat rooms?" But twenty years later, you’ll find that the internet has infiltrated every corner of life.

What’s truly interesting is: Agentic AI may not disrupt life in the form of “super assistants,” but instead, slowly integrate into everyday software—like Excel automatically completing your analysis, agents sorting priorities in your email, and customer service systems completely automating 80% of requests. At that time, you might not even call it “Agent,” but life will indeed have been transformed by it.

If I must say it’s hype, then the hype lies in—the market's promotion paints a picture that is 5-10 years ahead of what the realistic technology can accomplish.

Reference links:

Excessive hype + false packaging? Gartner predicts that over 40% of Agentic AI projects will fail by 2027 - 36kr

Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027

What is Agentic AI? | IBM

AI Agents vs Agentic AI: What’s the Difference and Why Does It Matter?

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