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Lets understand about Agentic AI
Agentic AI, also called autonomous AI, is a type of artificial intelligence that's designed to be more independent than traditional AI
With advancements in machine learning, NLP, and planning algorithms, AI systems are becoming increasingly capable of reasoning, adapting, and acting autonomously.
The current focus lies on developing AI that can act independently within a framework, set goals, and make decisions in the real world.
Capabilities of Agentic AI:
- Autonomy: Agentic AI can take action and make decisions on its own, within a certain scope, without needing constant human supervision.
- Complex goals: It can handle intricate tasks and workflows, understanding the bigger picture and breaking it down into steps.
- Understands Context: Agentic AI can understand the bigger picture and make decisions based on that context. It's not just reacting to isolated bits of data.
- Adaptability: Agentic AI can adjust its approach based on new information or changing circumstances.
- Intelligent decision support: Agentic AI can analyze vast amounts of data, identify patterns, and present insights to help humans make informed decisions. It can act as a valuable advisor in fields like finance, research, healthcare, and engineering.
How Agentic AI is different from Generative AI
- Generative AI: specializes in creating new data, like text, code, images, or music. It excels at tasks like generating realistic dialogue, writing different kinds of creative content, or composing music in various styles. Generative AI models are often trained on massive datasets of existing content to learn patterns and use them for generation.
- Agentic AI: Focuses on taking action and achieving goals in the real world. It excels at understanding complex situations, reasoning through problems, and making autonomous decisions within a set framework. Agentic AI leverages various techniques, including machine learning, natural language processing, and planning algorithms, to achieve its goals.
Here are some areas where agentic AI is being explored:
- HR: Automating recruitment tasks, filtering resumes, and scheduling interviews.
- Enterprise automation: Streamlining complex business processes and workflows.
- Customer service: Providing personalized support and resolving issues independently. Understand complex customer queries using natural language processing and respond with accurate and helpful information.
- Research: Could analyze vast amounts of research data, manage research workflows
Self-Driving Cars: The dream of autonomous vehicles is being driven (pun intended) by agentic AI. Self-driving cars require real-time perception, planning, and decision-making to Navigate complex road conditions
Manufacturing: Optimizing production lines, predicting equipment failures, and even managing inventory—agentic AI is streamlining manufacturing processes.
Healthcare: Assisting doctors in diagnosing diseases by analyzing medical images and patient data and Providing remote patient monitoring
Challenges for Enterprises Adopting Agentic AI
- Explainability: Understanding the reasoning behind an agentic AI's decisions is often complex, making it difficult to troubleshoot issues or build trust.
- Bias: If the training data is biased, the agentic AI system may perpetuate those biases, leading to discriminatory outcomes.
- Data privacy and security: Agentic AI systems often handle large amounts of sensitive data, making data privacy and security a critical concern.
- Ethical considerations: The autonomous nature of agentic AI raises ethical questions about responsibility, accountability, and the potential for misuse.
- Integration complexity: Integrating agentic AI systems into existing enterprise infrastructure and workflows can be challenging and time-consuming.
- Talent shortage: Finding and retaining AI talent with the expertise to develop and manage agentic AI systems can be difficult.
- Regulatory compliance: Enterprises must ensure that their use of agentic AI complies with relevant data privacy, security, and ethical regulations.
- Risk management: Identifying and mitigating potential risks associated with agentic AI systems is crucial, including the risk of system failures, data breaches, and reputational damage.
- Lack of AI Expertise: A shortage of AI talent within the organization can hinder development and implementation efforts.
Best Practices for Enterprises Adopting Agentic AI
- Start Small and Scale: Begin with focused use cases and gradually expand AI adoption.
- Build a Strong Data Foundation: Prioritize data quality, governance, and security.
- Foster a Data-Driven Culture: Encourage data-driven decision making and experimentation.
- Human-in-the-Loop: Maintaining human oversight in critical decision-making processes.
- Collaborate with Partners: Leverage external expertise and resources through partnerships.
- Ethical AI Frameworks: Developing and adhering to ethical guidelines can help mitigate biases and ensure responsible AI development.
- Monitor and Evaluate: Continuously assess the performance of AI systems and make necessary adjustments.
- Transparency and Accountability: Establishing clear roles and responsibilities for AI development and deployment.
- Continuous Learning: Implementing mechanisms for AI models to learn from new data and adapt over time can enhance their performance.
| Decision points | Open-Source LLM | Close-Source LLM |
|---|---|---|
| Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
| Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
| Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
| Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
| Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
| Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
| IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
| Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
| Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
| Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
In conclusion, while Gen AI is a valuable tool for automating tasks and generating content, Agentic AI holds the potential to revolutionize industries by enabling machines to act independently and achieve goals in the real world. As both technologies continue to advance, we can expect to see increasingly sophisticated and powerful AI applications emerge.
Fluid AI International Corporation (USA)