Quick summary: As AI continues to touch every corner of the world and automate almost every business function, Agentic AI systems will be the next step in automating complex business processes. Learn how Agentic AI or AI agents act on goals and decisions that will take business processes to the next and most advanced level, like a sci-fi movie. Read the blog below and understand how Agentic AI workflow is set to automate multiple steps within complex processes like receiving and processing claims, loan underwriting, or creating marketing campaigns.
It seems that Agentic AI will have a big run in 2025!
Why?!?!?!
Well, it holds a promising future! Because this technology is moving from THOUGHTS TO ACTION!
Over the last two years, the world has been boggled by the capabilities and possibilities that Generative AI services offer. One of its foundation models is LLM (Large Language Model) which can execute impressive tasks such as drawing conclusions and creating content across different models like text, videos, images, and audio on demand. However, Agentic AI systems – the next stage of GenAI hold a promising future for all.
Agentic AI systems are nothing but the new evolution from GenAI-powered tools to GenAI-enabled Agents. It uses the foundation model to render complex tasks across the digital world. Ultimately, as Mckinsey says GenAI is moving from thoughts or information to action, promising leading-edge productivity and innovation at scale. Think virtual workers are becoming capable of completing daily workflows with Agentic AI systems.
What is Agentic AI?
Earlier LLM frozen model-based generation lacked the iterative mechanism. The previous mechanism, called functioning calling, allowed developers to define the functions and actions of this model, such as making an order or performing calculations. Now, Agentic AI systems use this function-calling capability. It allows LLMs to manage intricate workflows with autonomy, utmost accuracy, and better quality. Since Agentic AI systems interact in a dynamic environment autonomously, this ability makes it Agentic.
That means it can act and invoke other technology tools to make decisions independently. These capabilities make Agentic AI systems different from others like RPA (Robotic Process Automation). Agentic AI gives a thought on whether it is needed to carry out certain actions or not in the first place. If it does, Agentic AI systems engineer the calling process to complete the task or adapt the change to the task. By carrying out these processes, Agentic AI learns from it and uses it in future tasks whenever required.
Agentic AI opens up possibilities, as it could work and act smoothly as a skilled virtual coworker. It can plan its actions, use available online tools, collaborate with other agents or people, and learn to improve its performance. For instance, virtual assistance can help plan and book a business trip, handling logistic operations across various travel platforms. Using day-to-day language, the programmers can describe a software feature to Agentic AI systems, which could then code, test, iterate, and implement the tool.
The product dynamics of the Agentic AI market
As per Emergen Research, if we look at the Agentic AI product dynamic, machine learning accounted for a major revenue share, and the trend will continue to remain the momentum for the forecasted period. Moreover, advancements in deep learning and neural networks are improving further advancements in AI-powered ML capabilities. This further highlights the effectiveness of evaluating huge volumes of datasets and increased computational power facilitating more precise and powerful models like Agentic AI – so, the future of Agentic AI seems bright and promising.
Its capability to consistently learn, adapt, and make decisions allows a new level of intelligent automation, ultimately driving a greater efficiency, cost-effectiveness, and sustainable competitive edge.
Early and top use cases of Agentic AI
- For software development, AI agents are increasingly being applied across specific business functions to automate customer service challenges and identify potential threats across the organizational network.
- AI agents also help enterprises manage projects, create reports, and generate new tickets for programmers like Google AI teammates and GitHub Copilot Workspace, which are already using AI agents.
- As per the recent study by Capgemini, 76% of organizations are looking forward to implementing Agentic AI systems for software development, making it the top and early use case of Agentic AI.
- Moreover, AI agents are being utilized to create, assess, and rewrite code, with 75% of companies planning to use Agentic AI workflow this way.
Agentic AI workflows
Across the industry and in different business functions, Agentic AI systems can manage workflow complexity at scale. It particularly outperforms legacy systems and conventional working styles when it comes to time-intensive tasks or tasks that require specialized qualitative or quantitative analysis. Agentic AI workflows are programmed to perform these tasks repetitively, disintegrating the intricate workflow and carrying out the subtasks across personalized instructions and data sources to achieve the targeted objectives.
The Agentic AI workflows commonly follow the below four steps.
Instructions by users
Using natural language prompts, the user interacts with the AI systems similarly when someone gives instructions to trustworthy employees. Afterward, the AI systems look for the calculated use cases, seeking more clarification as and when required to schedule other tasks and offer better outcome on demand.
Agentic AI plans, allocates, and executes tasks
Since Agentic AI systems process thoughts into actions, it schedule workflow by breaking it down into subtasks. The subagents assign these tasks to other subagents. These subagents hold expertise in every domain and necessary tools. It has codified domain knowledge and draws relevant conclusions from earlier experience to deliver the intended outcome. These subagents codetermine using business data and systems to carry out these assignments.
Agentic AI system improves output
Across the Agentic AI workflows, the agent may request additional information to offer relevant and accurate outcomes. The process reaches a conclusion by facilitating the final output, reiterating based on the shared feedback.
Agentic AI executes actions
The agents working on scheduled tasks take all the necessary actions and make the required decisions to complete the requested tasks with the utmost precision.
In agentic AI systems, the major building blocks are agents, business data or information, and the physical environment they interact with. Observing the circumstances, each of its agents operates independently, considering the possibilities, making necessary decisions, and taking necessary actions as and when needed to achieve the targeted goal.
Moreover, at the core of the Agentic AI systems is the shared memory, a repository that facilitates smooth coordination amongst all agents. The shared memory is like a HUB, as this is where agents and subagents exchange essential information, plans, and goals, benefiting from collective strategies and knowledge.
To create an efficient Agentic AI workflow, you need comprehensive knowledge about the individual elements that combine to form an entire system. By thoroughly considering and incorporating the building blocks, you can build Agentic AI workflows customized to your applications. The building blocks let you assemble in different configurations to achieve various functionalities.
The above four perspectives run in a loop, starting with planning and provisioning tools, followed by essential actions and memory, and then back to planning and beginning the next phase for other tasks.
What value can Agentic AI bring to businesses?
Like generative AI development services, Agentic AI unlocks the value by automating the long tail of complicated tasks. However, Agentic AI systems automate the tasks with more precision, characterized by exceptionally diverse and accurate outcomes that have been historically tough to address in a time- and cost-efficient manner against highly variable input. This GenAI-powered Agentic AI workflow simplifies the complicated and open-ended use cases in different ways, as mentioned below.
Agentic AI cleverly manages diversity
Many organizations follow sequential workflows, which means that a workflow can not progress until the previous steps are completed. This clarity makes the workflow easily codified and automated in a rule-based system; however, these systems are a bit fragile as they break down when certain situations are analyzed by a designer with definitive rules. A few workflows are less predictable, with twists and turns and several aftermaths.
These workflows require dedicated management and tactful handling, which makes rule-based systems tough. However, with Generative AI services enabled, Agentinc AI systems can manage a wide range of situations in any circumstance since they are based on GenAI’s foundation model. Agentic AI adapts and carries out specialized tasks in real-time and completes the process efficiently.
Agentic AI can be commanded with Natural Lanugage
When considering automizing the use case, it should be factored into a series of steps that can be codified. These steps are translated into code and integrated into software systems, which is a time-intensive, costly, and strenuous process that requires thorough technical expertise. Since Agentic AI systems leverage natural language for instructions, you can encode complex workflows quickly and easily.
This process is quite easy even for a person with limited technical knowledge, making it simple to integrate the subject matter experience, granting wide access to Generative AI services tools, and streamlining collaboration between technical and non-technical teams.
Agentic AI functions with existing software and tools
Agentic AI solution uses various tools and communicate across a broader digital network. It can be directed to work with any kind of software application, search on the web, gather and compile feedback, and leverage other foundation mode tools. Digital tool usage is Agentic AI’s USP in a way, but there is also a way in which their Generative AI services capabilities can also be applied seamlessly.
The foundation model learns how to interact with tools with natural language and other interfaces. You may need to put in external manual efforts to incorporate systems or manual efforts to gather output from different software to reach the relevant conclusion.
Potential use cases of Agentic AI
Code documentation and upgrade
AI agents have the capabilities to simplify code documentation and upgrade, as they can deploy specialized agents for legacy software experts, analyze old code, and document different program components. Simultaneously, the quality agents ensure review documentation and create test cases to help the Agentic AI systems iteratively refine their output and assure accuracy as per the organizational set standard.
This process is repeatable and could create a chain reaction, where agents’ framework components are reused for similar tasks across the organization while improving productivity and reducing software development costs.
Creating an online marketing campaigns
An agentic AI system connects the entire digital marketing ecosystem. It helps identify targeted users, ideas, intended channels, and other limitations in natural language. Afterward, the Agentic AI system helps develop, test, and integrate relevant marketing campaigns with the help of marketing professionals. Its agents tap on online surveys, analytics from CRM solutions, and other marketing research platforms to gather insights and develop best-fit strategies.
Moreover, the agents for copywriting, marketing, and design create customized content. These agents collaborate and iterate to improve the output and optimize the campaign’s impact while minimizing potential risks.
Loan underwriting
Agentic AI workflows have multiple agents, assuming certain tasks that could be designed to manage a wide range of credit-risk scenarios. Where human users could initiate the process with natural language and offer work schedules with predefined rules and standards. Afterward, the Agent team breaks down the tasks to work into executable subtasks.
How should business leaders prepare for Agentic AI?
Since the Agentic AI system is still in an emerging stage, diverting the budget toward this technology could bring you agentic systems, helping you achieve notable milestones. It is not too soon for business leaders to learn more about agents so they can consider which business functions can be accelerated with Agentic AI systems. Having this information on hand helps leaders create an informed strategy to remain ready for future challenges.
One such a use case is already identified. Organizations can now explore the different dynamics of agent systems leveraging APIs, tool kits, and libraries like Microsoft Autogen, LangChain, and Hugging Face, which help understand what is relevant and what is not. To prepare for the approaching wave of Agentic AI, business leaders can prepare their organization should consider the following three steps if Agent AI proves its potential successfully.
- Tabulation of relevant knowledge
- Strategic tech planning
- Human in the loop compliance mechanism
As per the recent survey by McKinsey on the State of AI survey, globally, 72% of organizations are deploying AI solutions, which shows significant growing interest in Generative AI services. Considering this increasing ratio, no wonder enterprises begin integrating Agent AI systems into their business processes and future AI roadmaps. Agentic AI-driven systems will remain a fruitful concept that is set to upgrade every industry, bringing an accelerated work environment.
Potential Impact of Agentic AI on Enterprises in 2025
Agentic AI is still in the early stages, and much development will happen before the world realizes its full potential. Moreover, because of its increased complexity, it poses certain challenges. Before deploying Agentic AI agents, an organization will require a certain number of tests, training, and coaching before it can be trusted to operate as intended. Even though Agentic AI systems are in their early stages, it is not difficult to imagine the endless opportunities that they could potentially offer in 2025.
Despite potential challenges, Agentic AI benefits are too significant to overlook. As research continues to progress, we expect to witness more sophisticated AI agents that can seamlessly collaborate with humans across different industries like sci-fi movies.