Unlocking Agentic AI: A guide to responsible implementation

AI agents could be unleashed within a matter of months if OpenAI boss Sam Altman's famously hyperbolic statements are to be believed. Agentic AI models move beyond simply processing data to actively learn, make decisions, and take action - offering potentially game-changing benefits to organisations that deploy them properly.

Earlier this year, NVIDIA announced its AI Blueprint: a new framework designed to help businesses create their own agentic AI applications. As CEO  Jensen Huang put it: “The age of agentic is here.”

Meanwhile, Google has unveiled Gemini 2.0, its latest AI model designed to push the boundaries of problem-solving using agentic AI.

From enhancing customer experiences to automating operations, this powerful technology has a range of upsides and use cases. However, it also raises key challenges, ethical considerations and implementation obstacles.

To find out more about how to overcome these potential problems, we spoke to Dharmesh Ghedia, Technical Director at digital consultancy Qodea, and asked for his insights and advice for organisations looking to deploy agentic AI.

How is agentic AI different from traditional AI?

"Agentic AI differs from traditional AI in having the capability to make decisions autonomously and operate independently with minimal human intervention. Earlier AI systems and assistants relied on human input for data processing, decision-making, and task execution within fixed parameters.

"Unlike these tools, which require prompts and generate responses based on existing data, performing a single task at a time, agentic AI powers autonomous agents capable of performing complex, multi-step tasks on their own. These agentic agents go beyond merely responding to prompts – they can make decisions, take action, and even adapt based on experience."

What kind of use cases is agentic AI best suited to?

 "Agentic AI is a powerful force for workplace transformation. It can automate routine tasks, reduce manual workloads, and empower employees to focus on higher-order responsibilities and get the big-picture work done with greater autonomy. 

"Retail is an industry poised to benefit from agentic AI. Think of an assistant AI that can autonomously track stock levels, predict demands based on historical data and current trends, and reorder supplies before shortages occur. If the AI detects a sudden spike in demand for a particular product, it can disperse inventory to the right shop at the right time, preventing stockouts. It could also identify slow-moving items and optimise pricing or promotions to clear excess stock.

"Beyond retail, agentic AI is powering logistics companies with autonomous systems that can anticipate potential delays on specific routes and proactively reroute shipments to maintain efficiency. Meanwhile, in finance, AI-driven risk management tools are continuously monitoring market conditions, detecting fraudulent transactions, and executing trades with minimal human oversight. The potential applications of agentic AI are vast, and as the technology evolves, its ability to drive efficiency and innovation across industries will only expand."

In what ways can agentic AI enhance the end-user experience?

"Almost three-quarters of buyers now expect personalised interactions, seeking applications and experiences tailored to their needs and can help them solve problems quickly. Agentic AI agents are uniquely equipped to deliver on these expectations – with their advanced reasoning and contextual understanding allowing them to anticipate customer needs and make real-time decisions. 

 "Take retail, for example. If a customer abandons their cart on an e-commerce site, an AI agent can immediately recognise this behaviour, send a reminder with a personalised discount offer, and even enable the customer to complete the purchase in a single click directly from the email.

"AI agents are also able to instantly retrieve precise information by crawling through existing data stores in seconds, surfacing the very specific answers customers need. For instance, customers could take a photo of an item like a drill, and an agent could quickly provide the technical details and prescriptive advice on how to use it by drawing on the company’s existing data stores.

READ MORE: Oracle's vision for transforming sales and supply chain with agentic AI

"Moreover, organisations will be able to use agents to prompt and anticipate future customer needs. If a customer places an order for 10 solar panels with a building materials company, an agent can converse with the customer to better understand their project. Through this interaction, the agent can envision the items the customer may need next, allowing companies to offer tailored deals.

"Unlike big, heavy AI systems, anyone can easily and quickly create an agent with prompt engineering and the right data. Solutions like Google’s Vertex AI Agent Builder can simplify the process, meaning a modest investment of code can develop into something quite sophisticated quickly. The accessibility of agentic AI will mean organisations of all sizes will be creating agents – the focus over the next year will be on scaling them."

What considerations for implementation should organisations take into account when scaling and implementing agentic AI?

"The benefits of agentic AI are incredibly promising for businesses. However, successful implementation requires thoughtful planning and integration. Whilst traditional AI provides a streamlined path for adoption, agentic AI typically requires deep integration into an organisation’s data architecture. It may also demand custom development to synchronise databases, software systems, and specific workflows. As such, it’s essential to allocate the right resources for a smooth transition. 

"Scalability is another key consideration. Agentic AI designed for one task may struggle when applied to a different domain. For example, an AI trained for customer service automation might need extensive retraining to function effectively in a financial risk assessment scenario. This lack of scalability can limit a system's versatility and increase development costs.

READ MORE: Is the world ready for an agentic AI revolution?

"But these considerations should not deter organisations looking to implement agentic AI models. Businesses simply need to make sure their algorithms are as efficient as possible, and that AI agents are built with the intent to scale in mind. Cloud-based platforms and services can offer secure and scalable solutions, allowing access to computational power without investing heavily in infrastructure.

"Organisations should also ensure AI architectures are modularly designed, enabling components to be repurposed or adjusted for different tasks. Transfer learning is another effective approach, which allows the AI to apply knowledge from one domain to another, minimising the need for retraining." 

What are the key steps organisations should take to unlock and leverage their data for use in agentic AI?

"The UK Government’s Business Data Survey revealed that while nearly all businesses handle virtual data, fewer than a quarter are truly extracting value from it. In addition, only 2% of businesses in 2024 used their data to support either AI or automated decision-making purposes, rising to 12% for large businesses. Without effective data management and integration, businesses won’t be able to provide the real-time, actionable insights required for agentic AI to make autonomous decisions and optimise workflows.

"Most companies are already sitting on a gold mine of data – an untapped opportunity to lay the foundation for agentic AI. To fully harness this potential, the time is now to prioritise data management. This means getting the fundamental aspects of data management in order to ensure information is integrated, accessible and actionable in real-time. By building a robust data infrastructure and adopting AI-ready data management strategies, organisations can unlock the full potential of agentic AI, enabling better decision-making and workflow optimisation while driving efficiency and innovation."

What ethical considerations do you believe will become more critical as agentic AI continues to advance?

"The automation potential of agentic AI raises concerns about job displacement and the need for workforce retention will be a concern for staff and users. Whilst agentic AI will drive efficiency and increase productivity, the impact of AI-driven automation will also require proactive policies and investment in education to achieve the benefits that come with the implementation of technology. It’s important for organisations to remember that AI augments, rather than replaces, human capabilities.

 "As part of the education element, understanding how an agentic AI system arrives at a decision is vital for building trust and ensuring accountability. Developing explainable AI (XAI) techniques will be paramount; documenting these and educating users will be key to ensuring users are aware of how errors are addressed and biases are prevented.

"Security is another crucial ethical consideration, as AI agents – like those managing personal emails, financial portfolios, or healthcare decisions – handle sensitive information. This makes agents vulnerable to hacking attempts by threat actors seeking to access this information or accidental exposure of data to unauthorised third parties.

"Resultantly, security teams must treat AI agents as a critical attack surface that requires constant monitoring and management. Implementing the right security measures and infrastructure – within a comprehensive cybersecurity strategy – is essential to safeguarding agentic AI systems.

 "Ultimately, agentic AI offers immense potential, despite its challenges. Organisations that tackle these ethical, infrastructural, and security concerns head-on can unlock unparalleled efficiency, personalised experiences, and groundbreaking innovation. Leveraging cloud services and strategic partnerships, these organisations can harness intelligent automation to shape the future. In short, dismissing agentic AI due to its inherent complexities would be a missed opportunity."

Have you got a story or insights to share? Get in touch and let us know. 

Follow Machine on X, BlueSky and LinkedIn