A common pattern in today’s AI adoption is that businesses are investing heavily in GenAI capabilities, yet many are leaving significant value on the table by failing to pair it with Agentic AI.
This matters because while GenAI can generate content, ideas, or responses, it can’t act on them. However, when combined with Agentic AI, your systems shift from being reactive and prompt-driven to autonomous and outcome-oriented.
This article aims to help senior technology leaders better understand the value Agentic AI can add to existing AI infrastructure, while outlining key considerations to determine whether its implementation aligns with your business needs.
Understanding Agentic AI
Agentic AI systems are designed to operate autonomously, perceiving their environment, making decisions, and executing actions without continuous human oversight.
This autonomy enables businesses to streamline operations, reduce costs, and enhance scalability.
The image above illustrates how the following systems function:
- Perception layer: This layer involves collecting data from various sources i.e. sensors, cameras, microphones, and digital inputs, to understand the current state of the environment. For businesses, this means real-time monitoring of operations, customer interactions, and market trends, facilitating proactive decision-making and rapid response to changing conditions.
- Cognition layer: Once data is collected, the cognition layer processes this information to interpret context, recognise patterns, and determine the best course of action. This analysis automates complex decision-making processes and reduces reliance on manual interventions, minimising the risk of human error.
- Action layer: After determining the appropriate response, the action layer executes the decision i.e. adjusting system parameters, initiating workflows, or communicating with stakeholders. This capability ensures quick implementation of decisions to enhance operational efficiency.
Ultimately, Agentic AI systems are equipped with learning mechanisms that improve over time.
Through techniques like reinforcement learning, where the system learns from the outcomes of its actions, and supervised or unsupervised learning, where it identifies patterns in data, the AI adapts to new situations and refines its decision-making processes.
GenAI is great at creation, while Agentic AI brings decision-making and execution
The most disruptive AI solutions over the next few years will combine GenAI with Agentic AI, and the key to achieving better business outcomes is knowing when to use one, the other, or both.
Not every use case requires a full agentic system, but many businesses will be surprised at how many should.
GenAI alone:
GenAI excels at generating new content from existing data patterns, significantly boosting creativity and productivity across various domains. In software engineering, tools like GitHub Copilot are increasingly used to automate routine tasks such as generating boilerplate code. This enables engineers to avoid repetitive work and focus on more complex, high-impact challenges. While a human-in-the-loop remains essential to ensure code quality and alignment, GenAI is already improving engineering efficiency and accelerating delivery.
In data science and engineering, GenAI supports teams facing surging data demands by improving accessibility and streamlining processing. Large language models (LLMs) help optimise how data is queried and managed, enabling faster and more accurate insights.
GenAI tools also enhance data analysis and application development by allowing users to interact with data through natural language processing (NLP), unlocking insights without requiring SQL expertise.
In the case of Retrieval-Augmented Generation (RAG), GenAI can efficiently access relevant data sources to assist customer service agents, or any customer-facing teams in responding to user queries with greater speed and accuracy.
Agentic AI Alone for autonomous decision-making and actions:
Agentic AI is best suited for automating complex, multi-step processes like supply chain management or customer service workflows.
Tasks that require real-time decision-making and adaptation to changing environments benefit from Agentic AI.
For example, a logistics company can implement Agentic AI to autonomously manage inventory levels, reorder supplies, and optimise delivery routes based on real-time data. This leads to improved operational efficiency, reduced errors, and faster response times.
Merging GenAI and Agentic AI:
The combined approach is best used when your business requires systems that generate content and autonomously act upon it.
The goal is to create end-to-end automated solutions that handle creation and execution, enhancing customer experiences through personalised interactions and proactive services.
An example is when an e-commerce platform uses GenAI to create personalised product recommendations. Agentic AI automatically adjusts pricing and inventory based on customer behaviour and purchasing trends.
This combined approach delivers better customer engagement through personalised and timely interactions, helping the business achieve greater agility in responding to market demands.
Why human oversight matters in enterprise-scale AI systems:
In high-risk decision-making environments like finance or healthcare, AI systems aren’t always the right decision, but when they do make sense to implement, it’s essential to maintain a human-in-the-loop approach. This is especially important for models prone to errors or “hallucinations,” as inaccuracies can have significant consequences in these sectors.
Industries governed by strict regulations often face challenges when implementing AI systems that lack transparency. The opaque nature of some AI models can hinder compliance efforts and erode stakeholder trust.
Moreover, deploying and maintaining advanced AI systems, such as GenAI and Agentic AI can be resource-intensive, requiring substantial computational power and specialised expertise. This complexity reinforces the need for human oversight to ensure smooth implementation and to mitigate potential risks that could strain organisational resources.
For organisations with limited resources, adopting simpler AI solutions that address specific needs without the complexity of full integration may be more practical. Certain tasks inherently require human intuition, empathy, and ethical judgment, which are qualities that AI currently cannot replicate.
Strategic considerations for tech leaders
As technology leaders, it’s essential to move beyond static GenAI chatbots and assess whether integrating GenAI and Agentic AI aligns with your organisation’s objectives. Evaluate the specific operational tasks within your organisation to determine if a combined AI approach is necessary or if a more straightforward solution would be more appropriate.
Ensure your organisation possesses the appropriate infrastructure and expertise to support complex AI systems. This includes robust data architectures, scalable computing resources, and a skilled workforce adept in AI technologies. Equally important is the prioritisation of AI solutions that offer transparency and explainability. In regulated industries, understanding the decision-making processes of AI systems is crucial to maintaining compliance and building trust with stakeholders.
If you’re exploring the potential of AI models or need support across AI engineering, MLOps, or data science, get in touch.
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