New Technology Trend 2025: Agentic AI 

Gartner has highlighted ‘Agentic AI’ as one of the top strategic technology trends for 2025, marking a significant shift away from conventional AI systems. While conventional AI excels at specific, set tasks, it often struggles with autonomous decision-making, contextual understanding, and adapting to dynamic business environments. Agentic AI overcomes these limitations by incorporating advanced capabilities such as autonomous goal-setting, contextual learning, and proactive problem-solving. In other words, this new paradigm allows AI systems to act more independently on behalf of organizations and individuals, comprehending broader business contexts and scopes all while taking initiative to achieve objectives. 

What is Agentic AI?  

Agentic AI is a type of AI software that operates autonomously to meet goals and tackles complex tasks assigned by humans. Unlike contemporary AI systems which are limited by their static nature, agentic AI assimilates human-like reasoning; it can analyze data, conduct research, organize required tasks, execute actions and improve and adapt based on experience in both digital and physical realms through APIs or robotic systems. Also, its “chaining” ability allows it to perform complex sequences of interconnected actions from a single request. Essentially, it’s a goal-oriented AI that leverages its tool-calling capabilities to interact with its environment, make decisions, and finish tasks with minimal human involvement.

Use Cases

This transformative technology is revolutionizing various industries through its ability to deliver efficient, autonomous, and personalized solutions for complex business challenges. Below are two examples of how AI agents were implemented to increase productivity and efficiency in their respective fields.

• End-user
  
  • Customer Service Sector: 7-Eleven in Thailand successfully manages 250,000 daily customer calls through AI-powered systems (Saunders, 2024). These bots use NVIDIA’s conversational AI platform to understand natural language inputs in Thai, process them, and generate contextually accurate responses (Lee, 2022). 
    
  • Financial Sector: AI-driven systems serve as machine customers and automated trading platforms, independently managing investment portfolios, executing market-responsive trades, and maintaining vigilant fraud detection protocols. For instance, JP Morgan has adopted this technology, helping it reduce fraud by 70%. This allowed the company to save $200 million per year. (Zharovskikh, 2024).
    

• For Companies
  
  • Client Onboarding: Consulting firm McKinsey built an AI agent that reduces email reading time by 90% and administrative tasks by 30%. The agent begins operating as soon as a client’s email arrives, extracting key details from the message and executing a series of tasks. For example, by analyzing past interactions with the client, it identifies the most suitable McKinsey associate to handle the case. It then drafts and sends a comprehensive email to the selected associate, providing all relevant information to ensure a seamless handoff. 
  • Information Management:  Web program Sharepoint’s agent functions as a customized AI assistant which helps find information in specific files. Similarly, Microsoft’s Teams Facilitator agent is expected to manage meetings from start to finish. 

Challenges and Risks

While AI agents promise enhanced efficiency, their implementation demands careful oversight and strict safeguards as well. 

• Gartner’s  AI Trust, Risk and Security Management (AI TRiSM): Companies face significant technical challenges outlined in Gartner’s AI TRiSM framework, including content anomalies, hallucinations, and vulnerability to ‘*smart malware.’ Therefore, robust AI governance strategies, integrating real-time monitoring, and secure model retraining processes should be implemented to ensure resilience, accuracy, and compliance while mitigating risks from emerging threats.

• Legal and compliance considerations: From intellectual property rights to consumer protection, organizations must establish strict guardrails through comprehensive policies and frameworks. This ensures AI agents remain constrained to defined roles and capabilities while maintaining security standards and data quality. Moreover, by red teaming, companies would be able to identify, test and address these challenges.

• Ethical concerns: The expected widespread adoption of Agentic AI also raises critical ethical concerns, including workforce anxieties about job displacement, data privacy vulnerabilities, and questions of liability for AI-driven errors and data breaches. Taking this into account, companies must prioritize transparent communication and accountability frameworks to reduce anxieties, protect rights, and ensure responsible AI deployment.

*smart malware: malicious software that uses artificial intelligence or machine learning techniques to adapt and operate more effectively

Road Ahead with Crowdworks

Advanced autonomous decision-making capabilities form the foundation of Agentic AI systems, enabling them to evaluate options, predict potential outcomes, and adapt dynamically to unexpected situations. By leveraging and cross-referencing insights from multiple large language models (LLMs), these systems continuously refine their problem-solving abilities. Thus, the effectiveness of Agentic AI heavily relies on the quality and reliability of its underlying LLM infrastructure—a principle at the core of Crowdworks’ innovations.

Crowdworks leverages the expertise of over 600,000 skilled human professionals to fine-tune pre-trained LLMs with high-quality, domain-specific knowledge or integrate RAG (Retrieval-Augmented Generation) for enhanced real-time decision-making and adaptability. Our Red Team services further identify risks, refine safety and compliance parameters, and provide iterative feedback to help companies develop reliable, ethical, and resilient agentic AI systems.

Trusted by over 550 global clients, Crowdworks combines exceptional LLM capabilities with Red Team expertise to deliver innovative, dependable, and impactful AI solutions, backed by a proven track record of success.

References: 

Aisera. (n.d.). Agentic AI: The future of intelligent automation. Retrieved November 22, 2024, from https://aisera.com/blog/agentic-ai/

Alvarez, G., Coshow, T., Sindhu, J. K., Ayoub, D., Horvath, M., Jones, N., Barot, S., Buytendijk, F., Resnick, M., Ray, B., Fabre, S., Sau, M., & Willemsen, B. (2024, October 21). Top strategic technology trends for 2025 (ID G00815761). Gartner.

Lee, Angie. (2022, February). CP ALL and conversational AI: Empowering digital transformation. NVIDIA Blog. Retrieved November 25, 2024, from https://blogs.nvidia.com/blog/cp-all-conversational-ai/

Saunders, Harrison. (2024, October). What is agentic AI? Fierce Network. Retrieved November 25, 2024, from https://www.fierce-network.com/cloud/what-agentic-ai

Zharovskikh, Anastasiya (2024, July). AI agents: Examples and applications. InDataLabs Blog. Retrieved November 25, 2024, from https://indatalabs.com/blog/ai-agents-examples