In the past year, significant investments in AI agents have been made. Industry leaders like Google, Microsoft and OpenAI have introduced several software libraries and frameworks supporting agentic AI and multi-agent applications. For example, Microsoft Copilot and Amazon Q are moving from knowledge-based tools to action-oriented systems. Even startups like Adept are building agent-based models with Generative AI. If we look at this rapid development, gen AI agents could soon become as popular as today’s LLM-based chatbots. In this article, we discuss how AI Agents will become the future of Generative AI.
Generative AI advancements have changed how we work, create and innovate. Generative AI applications use foundation models, such as large language models (LLMs) to generate content across various mediums, from text and audio to images and video. These advancements have made business operations smoother, from personalised recommendations to advanced problem-solving. Yet, the next phase of generative AI will bring more revenue-building streams for businesses.
AI agents take generative AI to a new level. Traditionally, creating agentic systems required intricate rule-based programming or bespoke training of machine learning models, which is resource-intensive. Foundation models trained on vast datasets can now understand unstructured natural language prompts and adapt to scenarios they haven’t been explicitly trained on. This flexibility eliminates the need for extensive preprogramming. To give you an idea, agentic systems are digital mechanisms capable of interacting independently in complex environments. These systems can plan tasks, collaborate with humans and other agents, leverage online tools and learn from feedback to continuously improve their capabilities. This means that AI agents can act as virtual coworkers, capable of performing tasks that demand sophisticated problem-solving and adaptability.
The value of agent-based systems for scaling businesses is their ability to automate complex use cases with highly variable inputs and outputs. These are tasks that, until now, have been challenging to address in a cost or time-efficient way. For example, consider a scaling e-commerce business that needs to manage customer inquiries, inventory updates and personalised marketing campaigns. We all know that handling these tasks often requires integration across multiple platforms, from customer relationship management (CRM) tools, and inventory databases to email marketing software. While some aspects of these processes have been automated, manual effort is still required to adapt to varying customer needs, track dynamic stock levels and prepare personalised outreach strategies. However, a diverse range of inputs and outputs with the need for real-time adjustments makes these tasks even more resource-intensive and complex.
But here’s the catch, Generative AI-enabled agents can make the automation of such intricate use cases smoother in three key ways:
Many business workflows follow a straightforward, linear progression with defined steps leading to a specific outcome, making them easily automated through rule-based systems. However, rule-based systems are prone to “brittleness”, they fail in scenarios not anticipated during design. More unpredictable workflows, with multiple twists, exceptions, and outcomes, demand nuanced decision-making and adaptability. Generative AI agents, leveraging foundation models, excel in these scenarios. They can adjust to diverse or unexpected situations and execute specialised tasks needed to complete the process.
Traditional automation requires breaking a use case into rules and codifying them into software systems, a process that is labour-intensive and requires technical expertise. Agentic systems simplify this by accepting natural language as input. This enables even complex workflows to be translated into actions with minimal technical know-how. Subject matter experts can contribute directly without relying on software engineers, democratising access to AI tools, enhancing collaboration between technical and non-technical teams, and accelerating the integration of expertise into workflows.
Beyond analysing and generating insights, agent systems can interact with software tools and function across broader digital ecosystems. For example, agents can operate plotting tools, retrieve and organise web data, gather user feedback, and utilise other foundation models. By learning how to interact with tools via natural language or other interfaces, foundation models eliminate the need for manual system integration (for example, using ETL tools) or piecemeal aggregation of outputs.
Generative AI-enabled agents can support intricate use cases across various industries and business functions. They excel in workflows involving complex, time-intensive tasks or requiring diverse qualitative and quantitative analyses. These agents achieve their objectives by systematically breaking down workflows into subtasks and leveraging specialised instructions and data to achieve the desired goals. The process typically involves four stages:
The process begins when a user interacts with the AI system through a natural-language input, similar to instructing a trusted employee. The agent identifies the intended use case from the prompt and, if necessary, seeks clarification to ensure it fully understands the user's requirements.
Once the prompt is processed, the agent system formulates a workflow by dividing it into tasks and subtasks. A manager subagent delegated these assignments to specialised subagents with domain-specific knowledge and tools. These subagents draw on codified expertise, past experiences, and relevant organisational data to complete the tasks while maintaining smooth coordination.
As the tasks progress, the agent system actively engages the user for feedback or additional input to ensure the results align with expectations. This iterative refinement ensures the outputs are accurate, relevant, and aligned with the original request.
Once the workflow is completed and approved, the agent carries out any required actions to fulfil the user’s request. The execution phase ensures that the requested task is fully resolved and meets the user’s objectives.
AI agents are still in their early stages of development but investing in generative AI is a strategic move that can help businesses transition into the future of AI-driven agents. This could help businesses improve their overall operations, decision-making and even open new revenue streams. Check out our latest article on Strategic Generative AI Investment to learn why investing in Generative AI could be the next big move any scaling business or enterprise can take in 2025.
However, adopting generative AI comes with its own set of challenges. Developing reliable multi-agent AI systems to ensure secure interactions within complex networks and integrating agents across diverse ecosystems remain significant hurdles. The first thing any organisation needs to do is carefully evaluate its strategy for generative AI adoption to address these technical complexities. At NexGen Labs, we do exactly that. We help you identify the challenges of adopting Generative AI, cloud infrastructure and high-performance computing. Our expert team offers comprehensive support, from strategic planning to proof-of-concept development, so businesses of all sizes can adopt Generative AI with confidence.
Curious how we make it happen? See for yourself below!
AI Strategy Consulting: We provide personalised guidance, readiness assessments, and strategic roadmaps to ensure seamless AI adoption tailored to your unique needs.
High-Performance Infrastructure: With NexGen Cloud’s cutting-edge NVIDIA-powered infrastructure, we enable efficient model development for demanding workloads.
R&D Partnerships: Gain access to the latest hardware and dedicated clusters to drive innovative research and accelerate breakthroughs.
Technical Training & Workshops: Expert-led sessions on AI model training, data engineering, and machine learning.
Infrastructure & Deployment Advisory: Implement scalable, sustainable and cost-effective solutions with our GPU-as-a-service offerings designed to grow your business.
With NexGen Labs, you’ll have the right tools, technical expertise and support to adopt Generative AI. Speak to an Expert to Get Started with Generative AI
Generative AI agents are systems that use AI models to perform tasks, make decisions, and adapt to complex environments independently.
They automate complex workflows, enhance operational efficiency, and open up new revenue streams through innovative solutions.
AI agents benefit industries like e-commerce, healthcare, finance, manufacturing, and marketing by streamlining workflows and enhancing decision-making.
Yes, with proper safeguards and security measures, AI agents can be integrated securely into business operations to protect sensitive data and ensure safe interactions.
NexGen Labs provides end-to-end support, including strategy consulting, infrastructure solutions, R&D partnerships and technical training.