Executive summary:
Agentic AI (AAI), or AI agents, are artificial intelligence instances that can take autonomous action on your behalf. AT Talbot West, we’re big proponents of agentic AI, so long as it has the proper guardrails in place for oversight and explainability. We believe that AAI can drive massive efficiencies for enterprise.
For example, specialized AI agents can serve important roles in our cognitive hive AI (CHAI) architecture. CHAI is a modular paradigm that we’re evangelizing because it’s more configurable, explainable, and adaptable than monolithic LLMs. And agentic AI fits seamlessly into CHAI because CHAI’s traceability gives human insight into whether agents are acting according to their mandates or not.
Talbot West specializes in guiding businesses through the complexities of implementing advanced AI technologies, including agentic AI and CHAI architectures. Our expertise helps organizations navigate challenges and maximize the benefits of these AI innovations. If you’d like to explore agentic AI—or a modular CHAI implementation—contact us for a free consultation.
Agentic AI makes decisions and takes actions without constant human oversight. Think of it as a personal assistant who not only follows instructions but also anticipates needs and solves problems independently, while continuously learning to improve.
Agentic AI combines several advanced AI techniques, including machine learning, natural language processing, and reinforcement learning. These techniques allow an AI agent to perform tasks independently, learn from experiences, and make decisions based on the data it receives from its environment.
The following traits illustrate how agentic AI systems differentiate themselves from other types of artificial intelligence.
Agentic AI systems make decisions and execute actions without needing constant guidance or oversight from humans. They continuously analyze data and take appropriate actions on their own. This autonomy allows them to manage and optimize complex processes, such as supply chain logistics or customer relationship management.
This level of autonomy means that businesses can operate more smoothly and efficiently because AI systems handle tasks that would normally require human attention, freeing up staff to focus on more strategic work.
Agentic AI can adapt to new and changing environments
Unlike traditional AI which follows static, pre-defined rules, agentic AI can learn from its experiences and modify its behavior based on feedback and outcomes. For example, if an AI-powered sales system notices that a particular strategy is not effective in a certain market, it can adapt by trying different approaches.
This adaptability makes agentic AI highly suitable for dynamic environments where conditions can change rapidly, such as stock markets or customer service scenarios.
Agentic AI is not just reactive. It can anticipate needs, identify potential problems, and take action before being explicitly directed to do so. For instance, in a manufacturing setting, an agentic AI system could detect signs of machinery wear and schedule maintenance before a breakdown occurs. This proactive nature ensures that potential issues are addressed early to enhance operational efficiency and reduce risks.
Context-awareness allows agentic AI to understand and interpret the environment in which it operates. It can make decisions that are data-driven and also contextually appropriate. For example, a customer service AI might recognize a customer’s frustration from past interactions and adapt its tone and response accordingly. When it understands the broader context, agentic AI provides more accurate and relevant outcomes that improve user satisfaction and engagement.
Continuous learning is a fundamental trait of agentic AI. It enables it to improve its performance by learning from both its successes and mistakes and refining its algorithms and decision-making processes. For example, an AI managing online advertisements could learn which ads perform better in different demographics and adjust future ad placements to optimize engagement and conversion rates. With this continuous learning capability, the AI system remains effective and relevant, ready to adapt to new data and evolving requirements.
Non-agentic AI does not make independent decisions or take actions without human guidance, and often is not capable of taking actions. Unlike agentic AI, non-agentic AI usually gives outputs, rather than taking actions, and follows pre-defined rules or scripts. Non-agentic AI includes chatbots and virtual assistants that respond to specific commands, as well as predictive models that analyze historical data to forecast outcomes.
Here’s a comparison between agentic and non-agentic AI.
| Agentic AI | Non-agentic AI |
|---|---|
Operates independently, making decisions without human oversight. | Requires human guidance and input; does not operate autonomously. |
Takes actions | Gives outputs but (usually) not able to take actions. |
Learns from experiences and adapts to new situations in real-time. | Follows pre-defined rules or models; lacks the ability to adapt without human intervention. |
Takes initiative to perform tasks or solve problems proactively. | Reacts to specific commands or inputs; does not initiate actions on its own. |
Understands and considers the context for more nuanced decision-making. | Operates based on fixed rules or specific inputs, often without understanding the broader context. |
Continuously learns and improves from successes and failures. | May improve through supervised learning but requires human input for training and adjustments. |
Autonomous vehicles, dynamic pricing, supply chain management. | Chatbots, virtual assistants, recommendation engines, predictive analytics. |
Capable of making complex, autonomous decisions based on real-time data. | Provides insights or responses based on programmed logic or historical data; decisions are made by humans. |
Agentic and non-agentic AI serve different purposes and complement each other.
If you’re unsure how to implement AI systems—agentic or non-agentic—in your business, Talbot West is here to help. Our team tailors AI solutions to your specific needs, whether it’s enhancing decision-making or streamlining routine tasks.
Reach out to us today to explore how AI can transform your operations.
Cognitive hive AI (CHAI) is a modular AI paradigm that outperforms monolithic AI systems in explainability, adaptability, security, configurability, and governance. The term “cognitive hive AI” was coined by Talbot West co founders Jacob Andra and Stephen Karafiath to evoke the collective intelligence of a honeybee swarm (more about the hive analogy here).
CHAI addresses many of the limitations and concerns associated agentic AI implementation. By leveraging CHAI, organizations can harness the power of agentic AI while ensuring safety, explainability, and rapid deployment.
CHAI's modular structure allows for the seamless integration of agentic AI components within a larger, more flexible framework. Importantly, CHAI isn't limited to agentic AI: it can incorporate a diverse array of AI technologies and knowledge management systems, including:
This versatility allows organizations to create custom AI ecosystems tailored to their unique needs. The integration of agentic AI within this diverse framework offers the following benefits:
By embracing CHAI, organizations can confidently implement agentic AI solutions that are not only powerful and autonomous but also safe, explainable, and aligned with business objectives.
The deployment of agentic AI requires careful considerations of regulatory compliance issues, ethical implications, and technical constraints.
Cognitive hive AI (CHAI) offers solutions to many of the challenges associated with agentic AI implementation:
Talbot West recognizes the challenges of AI implementation and can help your business navigate the complexities of implementing agentic AI. We specialize in helping businesses deploy CHAI AI architectures in a safe and stepwise fashion.
Our services cover everything from data preprocessing to feasibility studies, pilot projects, and full-scale deployment of AI systems. We also provide bespoke training and education services for upskilling your workforce to be AI-adept.
Whether you're looking to optimize your supply chain, improve customer service, or leverage AI for data analysis, Talbot West provides solutions to meet your specific needs and drive innovation.
Schedule a free consultation with our AI experts. We’ll tailor our recommendations to your use case to ensure you have a robust AI-driven solution that benefits your business.
Generative AI:
Agentic AI:
Overlap:
Examples in the overlap:
At Talbot West, we recognize this nuanced relationship between generative and agentic AI. Our CHAI (Cognitive Hive AI) framework is designed to accommodate and leverage both aspects, allowing for the development of sophisticated AI systems that can be generative, agentic, or a combination of both, depending on the specific use case and requirements.
This flexibility in our approach ensures that we can provide tailored solutions that maximize the benefits of both generative and agentic capabilities, creating more powerful and versatile AI systems for our clients.
Agentic workflows in AI refer to multi-step processes managed by intelligent systems that operate autonomously to complete complex workflows. These workflows often involve a combination of routine and higher-value tasks, allowing AI to make informed decisions and optimize operations in real-time.
For example, agentic capabilities are employed in fraud detection, supply chain management systems, and enterprise systems to automate repetitive, mundane tasks and streamline intricate tasks. Agentic workflows help businesses reduce reliance on human workers, improving efficiency and productivity.
The agentic AI market is rapidly expanding as more industries adopt autonomous agents to enhance operational efficiency and strategic decision-making. The market includes applications across sectors such as finance, healthcare, and logistics, where intelligent systems manage complex workflows and provide robust language understanding for informed decisions.
With the growing demand for AI systems that can handle a wide range of tasks autonomously, the market for agentic AI is expected to see significant growth. This expansion reflects the increasing need for AI solutions that go beyond simple automation to offer proactive and adaptive capabilities.
The best AI model depends on the specific use case and desired outcomes.
For tasks requiring human-like text generation or content creation, large language models such as GPT-4 excel with their robust language understanding. For applications involving autonomous decision-making, goal achievement, and navigating complex environments, agentic systems that enable agentic workflows are more suitable.
At Talbot West, we believe that the best model is not a single model, but a modular architecture that we call “cognitive hive AI” or CHAI. See why in our article about CHAI.
AI can predict human behavior to a certain extent by analyzing patterns from vast datasets. AI systems use advanced algorithms and machine learning models to identify trends and forecast outcomes in different contexts, such as consumer applications, fraud detection, and decision-making processes.
While AI models can offer insights and informed predictions, they are not flawless in predicting complex, nuanced human behavior. The accuracy of these predictions often depends on the quality of data and the AI’s holistic understanding of human language and social cues.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
