Agentic AI is the next big leap towards enterprise AI.

This AI word may be newer, but leaders like you always seek to expand their AI ambitions, which are never newer. Rather, they are familiar, which aims to expand the current capacity of AI tools and gain more for enterprise workflows.

This is where agentic AI fits in. Every AI tool is a better version of its previous model.

Thus, agentic AI possesses greater possibilities than LLMs or conventional AI can do for enterprises.

Of course, LLMs are better than conventional AI, which helps solve automation needs for repetitive tasks only when programmed with predefined scripts.

With better attributes than conventional AI, LLMs can generate texts for enterprise workflows, such as employee support scripts, or craft personalized responses for requesters. However, LLMs can only work when prompted, so they meet narrow scopes of enterprise needs—not end-to-end.

Coming to agentic AI, it possesses autonomy, intentionality, adaptability, and decision-making capabilities.

By taking ownership of a task, defining objectives, understanding the goals behind a task, and adapting to new data, agentic AI handles enterprise workflows with minimal human intervention.

This makes agentic AI handle tasks from start to finish—be it complex, muti-step, or subtasks.

Agentic AI is an autonomous and independent phenomenon that aims to take current AI systems or solutions to a new level and reimagine enterprise workflows.

In this blog, we’ll walk you through a comprehensive guide to agentic AI and help you understand its potential for enterprise workflows.

An advanced AI system with the autonomy to make decisions, define goals, and adapt to new and evolving inputs or data to complete tasks end-to-end is an agentic AI.

It solves problems through understanding NLP and exhibits human-like abilities to plan, adjust as circumstances change, apply reasoning, and set goals to complete a task with limited human intervention.

Let’s say a user needs help with home loan applications from his company. When he asks non-agentic AI or GenAI to offer help with these applications, it can provide typical process guidance but has no actual intention of helping him apply.

With agentic AI workflows or AI, the system easily understands the end goal. It can make autonomous decisions to provide help until a satisfactory answer is delivered in the following manner

• Check the company systems and analyze their eligibility

• Perform eligibility checks

• Proceed further if users qualify

• Suggest document uploads for verification

• Recommends personalized schemes, including terms and loan repayment and interest

• Populate forms to apply for a home loan

• Keep users updated and suggest next steps

Let’s find out how agentic AI systems exhibit core attributes through the above example

• Autonomy: Agentic AI initiates action by allowing the user to apply for a home loan without manual intervention

• Reasoning: It applies reasoning to adjust to more suitable eligibility criteria and offer an appropriate loan amount within his income range

• Intelligence: Agentic AI systems intelligently observe the interaction to make independent decisions and help manage subtasks or workflows, such as helping apply for suitable schemes or suggesting to apply later when attaining eligibility

Based on the agentic AI features, leaders can ramp up the current pace of enterprise workflows and gain maximum value with limited human intervention.

Conventional AI and agentic AI have striking differences from each other.

Conventional AI:

Conventional AI excels at:

• Predictable events

• Routine tasks

• Automating repetitive problems with predefined instructions

However, conventional AI struggles with:

• Complex workflows

• Multi-step enterprise workflows

• Lack of knowledge and understanding of these scenarios

Agentic AI:

Agentic AI is adept at:

• Independent and proactive task handling

• Adaptability to predictable and unpredictable scenarios

• Complex and multi-step workflows

• Back-and-forth iterations for subtasks

With large language models and massive datasets, including integrations with enterprise systems, agentic AI can gain autonomy, intentionality, adaptability, and reasoning to unlock back-and-forth iterations for subtasks and complete complex or multi-step workflows. Agentic AI is more independent and less human-dependent.

Leveraging agentic AI unlocks efficiency, productivity, and creativity and lowers costs.

Agentic AI follows intricate design patterns to help end users from start to finish through an enterprise workflow.

It completes a task using reflection, planning, multi-agent collaboration, tools, and back-and-forth communication techniques.

• Once a prompt is raised, an agentic AI uses reflection to review its code and fix errors.

• It combines multi-agents or LLMs to develop work planning.

• Using third-party tools or integrations, agentic AI boosts comprehension for an input.

• Depending on fast token generation, it can gain contextual memory, carry forward with follow-up conversions, and complete a task.

With integrations and human oversight, LLMs can become more powerful and translate into agentic AI. The power of agency in LLMs helps companies to maintain a task from start to finish and gain instant results.

Leaders want powerful AI systems that can handle intricate and complex enterprise workflows rather than just narrow, repetitive tasks.

Leaders feel the urge to leverage AI systems with agency-like characteristics that humans inherit.

Agency is a natural phenomenon that enables humans to have autonomy, decision-making capabilities, adaptability, intentionality, or objective orientation to work independently with minimal external output.

So, LLMs or GPT-4 systems are designed to make API calls with enterprise systems or integrate with knowledge articles, external resources, third-party repositories, or massive datasets to gain agentic abilities, leading to the development of agentic AI.

For example, AI copilot is a path forward to enterprise AI, which unlocks immense possibilities for solving domain-specific workflows collaboratively with humans.

From thereon, agentic AI is the next level AI frontier or a giant leap forward in AI copilot or custom GPTs, which can work independently depending on AI algorithms and deep learning systems with minimal human intervention.

Conversational AI is limited in domain-specific abilities. Agentic AI addresses enterprise challenges by meeting the needs of a wide range of complex or multi-step workflows, eventually reducing human workloads.

What agentic AI can do for you

1. Increased efficiency with autonomous decision-making

Agentic AI systems, with some similar characters like AI copilots, can make independent decisions about a task, set goals, and complete it.

For example, if a user needs help with password resets, an agentic AI interface can decide how to offer help from start to finish. If the user cannot solve the problem using a provided password reset link, the AI system instantly surfaces other options, such as ‘Connect me with an agent.’ and ‘Reset MFA,’ etc.

This reduces human assistance, increasing productivity for your employees and service desk agents.

2. Enhanced human-AI collaboration for interpreting nuanced natural language

Agentic AI efficiently develops natural language understanding by being trained with massive datasets and large language models, enabling it to interpret nuanced or complex NLP queries better. The ability to comprehend multi-agent conversations can solve user problems autonomously and reduce the workloads of repetitive tasks for agents, allowing them to focus more on creative problems.

3. Improved real-time analysis and adaption

The inherent adaptive ability allows agentic AI to move between subtasks and adapt to changing scenarios to provide appropriateness and complete the tasks with full context.

For example, you have to create a knowledge article on printer issues. You ask agentic AI to do it for you. It creates an outline and then asks follow-up questions to modify the task.

For example, you have to create a knowledge article on printer issues. You ask agentic AI to do it for you. It creates an outline and then asks follow-up questions to modify the task.

4. Increased user experience

Unlike traditional AI, agentic AI can help your people with cross-functional tasks, subtasks, and even multi-step complex workflows–all in a human-centric way. Employees can save time and increase efficiency and productivity, allowing them more convenience and flexibility to thrive at work. On the other hand, agents can free themselves from repetitive and mundane tasks and resolve critical problems to elevate user experience.

Thus, agentic AI can augment and add value to tasks by reducing errors and helping produce the right outcome for any business queries.

HR, IT, operations, finance, marketing, and legal, among others, can optimize business operations and gain maximum tangible value.

Other than response generation and content creation, agentic AI unleashes potential similar to that of an AI copilot, which completes a task from start to finish and reduces the workload on human agents.

Agentic AI, Some essential use cases for you

1. IT support

Service desks can free up agents and allow employees to autonomously handle IT-related requests or support issues.

Agentic AI provides more than over-the-surface assistance. This means users can still find help if knowledge resources lack adequate information. Agentic AI can learn incidents from past interactions and reason to give answers to new queries.

Without human intervention, employees can handle IT support issues such as password resets, account unlocks, software installs, hardware upgrades, asset requests, and cross-functional support.

For example, a knowledge resource may contain instructions for installing a specific software version. If users need assistance installing a different software version, agentic AI can guide them through installation to deployment based on the operating system and software version.

2. HR support

Employees need assistance with HR support issues daily, which can be stressful for HR admins. Enterprise AI, like AI copilot, unleashes agentic AI properties to some extent to control HR tasks and help find answers to HR queries without waiting for long days or months.

Essential HR operations, admin tasks, and even routine tasks are no longer pending. Employees instantly find answers to domain-specific questions about payroll, onboarding/offboarding, expenses, reimbursements, insurance, PTO, etc., in real time.

For example, offboarding involves so many subtasks. It can encompass user de-provision, knowledge transfer confirmation, final settlement, feedback collection, hand-over of relieving letters, etc.. Agentic AI can automate and efficiently complete these tasks without human intervention.

3. Customer support

Besides internal support, agentic AI efficiently handles external support or customer support tasks.

With agentic AI, you can manage more than just personalized communications; you can take it further by completing tasks from start to finish.

A customer asks the agentic AI interface, ‘Why was money debited from his associated account even after pausing a subscription? '

Agentic AI studies past interactions in the system and checks for discrepancies. If a pause option were selected within a week of the subscription approaching, money would be debited, and hence, the condition would apply to the next installment.

Despite being a complicated issue, a customer can find the answer and refrain from calling a human agent.

These are some of the handful of use cases a business can try. Depending on the business needs, you can customize your workflows and manage use cases from start to finish using agentic AI or AI copilot —without the hassles of missing significant productivity working hours.

As we already mentioned, new powerful AI models are built on their previous version of AI models; LLMs or GPT-4 models can also attain the power of agentic AI.

On top of these models, we need external knowledge support to expand the possibilities of agentic copilots or agentic AI systems.

• Integration – To gain domain-specificity and correctness of answers for business-related issues, you can integrate business systems or tools with LLMs and turn them into an agency capacity.

• Knowledge resources – You have resources spread across your website, intranet, and other tools. You can ingest these knowledge resources into your LLMs to boost their search function and information retrieval capacity.

Besides knowledge ingestion and system integration, model fine-tuning can also help.

This can mean that the integration success and richness of data in knowledge resources are essential for providing contextual and intent-based answers that can expand agentic AI's capacity and help boost enterprise workflows' efficiency.

Like other AI systems, agentic AI can pose threats. It requires extra caution from users or leaders.

Agentic AI can raise two major risks for us,

• Blackbox theory: The secret to how AI works is still unknown. The black box theory makes it tough for everyone to know the inside workings of AI systems. We can only view the inputs and outputs. AI trusts can be at stake.

• Independent decisions: Agentic AI can set goals and make independent decisions to provide recommendations, which can be flawed and cause problems.

The responsible use of agentic AI can diminish flaws and major risks.

You must follow these instructions to maximize agentic AI for enterprise workflows-

• Ensure data used to train models are correct.

• Put constant monitoring to prevent misjudgment and flaws.

• Keep human-in-the-loop systems to secure AI’s decisions.

AI safety guardrails are important. We must ensure they work in their best capacity and boost AI trust to help enterprise workflows solve growing business problems.

Enterprise workflows are complicated. Performing one or two tasks is not enough. To achieve success, enterprise leaders want to accomplish an entire workflow.

Agentic AI transforms the existing phenomenon of LLMs. It is poised to advance current business processes and help accomplish more tasks in less time.

So, agentic AI expands the capacity of enterprise AI by integrating domain-specific knowledge resources and business systems into your LLMs or GPT-4 models, thus achieving planning, reasoning, adaptability, and decision-making capabilities at large. This extended agency gives AI systems autonomous power to accomplish tasks efficiently without human intervention.

Workativ helps you design your agentic AI systems by easily integrating third-party resources and business systems. Using our no-code LLMs/ Generative AI or Knowledge AI builder, you can gain agency inside your AI systems and turn enterprise workflows into flexible workflows for your employees, driving efficiency and productivity.

AI is a technology that continues to evolve. Embrace it to gain competitive advantage and usher in the wave of agentic revolution with the right implementation strategy with Workativ.

1. What is agentic AI?

Agentic AI is a new phenomenon that unleashes more than automation to complete a complex workflow from start to finish using autonomy, intentionality, adaptability, and planning with minimal human oversight.

2. How does agentic AI help businesses?

Agentic AI automates yet drives workflow efficiency, improves human-AI collaboration, reduces human effort, and boosts productivity through adaptation for various business use cases such as IT support, HR support, and customer support.

3. How can I build agentic AI?

You can give your LLMs the power of agentic AI with autonomy, planning, and adaptation through the integration of business systems and knowledge resources. Workativ’s no-code Knowledge AI helps you gain simple processes to design your workflows and layer them with agency properties.