As artificial intelligence continues to transform industries, integrating AI into your operations is no longer optional—it's a necessity.

As CIO’s, over the last few months you've likely seen the movement from RPA (Robotic Process Automation) to using LLMs/co-pilots to automate business tasks. We're here to introduce the next frontier in the business automation journey which is advanced, human-like AI called Agentic AI.

Agentic AI adapts, learns, and addresses complex business challenges, making your operations more intelligent and efficient. Incorporating Agentic AI into your business strategy is crucial to remaining competitive.

In this article, you'll discover everything you need about Agentic AI, including its types, architecture, benefits, and how it can impact your business.

What is Agentic AI?

Agentic AI refers to a mesh of AI agents that are able to execute tasks end to end across enterprise applications. These agents are able to learn, iterate and improve their performance behaving very similarly to an actual human employee. In contrast to LLMs and co-pilots that primarily focus on retrieving information or creating simple documents and are typically limited to very select use cases, Agentic AI can take action with little human involvement and execute complex workflows.

Lets understand this with an example of responding to an RFP. Today large enterprises employ 100s of people that are tasked with responding to RFPs. Each RFP can take multiple days, and sometimes even weeks to compose. This presents a natural use case for automation. Lets look at how an LLM/co-pilot would help create a proposal response from scratch.

LLM Approach

• Input query, upload 1-2 previous RFPs
• Manually collate and input information about the customer
• Ask LLM to generate an output

This would then take a human employee multiple hours to iterate on in order to make the proposal relevant to the customer.

Let’s now take a look at the Agentic AI approach.

Agentic AI approach

• Ingest 1000s of previous RFPs as well as product documentation by integrating with internal databases
• Ingest information from URL of the customer and customise response for that customer
• Ingest instructions given in natural language to create commercials, SOW, pricing, terms of service, etc
• Take feedback from humans, iterate, and improve with each successive proposal

You can learn more about how Agentic AI is transforming proposal writing for large enterprise customers across the globe in this Youtube video

Key Characteristics of Agentic AI

AI agents possess unique capabilities that set them apart from traditional AI systems, enabling them to operate with greater autonomy and intelligence. Here are the key characteristics that define AI agents:

1. Tools: AI agents are equipped with a diverse range of tools, such as search engines, calculators, code interpreters, and more. These tools allow them to efficiently gather information, process data, and execute specific tasks, making them highly versatile across various applications.

2. Memory: AI agents can store and retrieve both short-term and long-term information. This feature allows them to learn from past experiences, adapt to new situations, and make more informed decisions over time. Their memory enables them to build on prior knowledge and improve their performance in real-world applications.

3. Planning: One of the defining characteristics of AI agents is their capacity for planning. They can break down complex tasks into manageable steps, create structured plans, and execute them logically to achieve their objectives. This allows them to handle intricate problems and work toward long-term goals with minimal human intervention.

4. Action: Unlike traditional AI systems, AI agents can take action in the real world or within simulated environments. They can interact with software, handle data, or manage physical systems to accomplish tasks, making them capable of decision-making and execution.

5. Reflection: AI agents can evaluate their performance and identify areas for improvement. Through human feedback, they analyze their actions, assess the results, and adjust their approach to optimize outcomes. This self-improvement capability ensures that AI agents become more efficient and effective.

These key characteristics enable AI agents to function autonomously, adapt to changing conditions, and continuously improve performance, making them a powerful asset in various industries and applications.

Let’s understand how Agentic AI actually works.

How Does Agentic AI Work?

At the heart of Agentic AI are sophisticated decision-making processes that weigh options, anticipate outcomes, and respond effectively to challenges. These systems integrate multiple large language models cross-referencing insights to enhance their problem-solving capabilities. Designed for dynamic environments, they adjust real-time strategies, optimizing performance as they gather feedback.

Machine learning is crucial in Agentic AI, enabling systems to learn from data, recognize patterns, and make predictions. This continuous learning helps AI agents tackle increasingly complex challenges. The process typically unfolds in stages: the user provides instructions, the agent system plans and executes tasks, and refines outputs based on feedback before final action.

Agentic AI is built on three main components: prompts, memory, and tools. The prompt sets the agent's objectives and methods; memory allows the agent to learn from previous interactions, and tools (like APIs or functions) help the agent execute tasks efficiently. These components are integrated into single-agent or multi-agent systems, with the latter offering better scalability, collaboration, and fault tolerance.

Let Ema handle the tasks so your team can focus on what matters.

Single-agent systems are easier to manage but limited in scope, while multi-agent systems allow specialization and improved performance, especially in complex or evolving environments. Together, these systems offer a robust framework for intelligent automation.

But what are the challenges of implementing Agentic AI in the enterprise?

Challenges Of Agentic AI in the Enterprise

Applying Agentic AI in an enterprise environment presents significant challenges. One of the main concerns is the risk associated with automated dynamic workflow generation, where errors—like incorrect workflows—can be costly, especially given the complexity of enterprise technologies.

Read this article, Rise of the Omniglot: Agentic AI to Tackle Enterprise Automation

However, the potential benefits have driven multiple tech vendors, from industry leaders to startups, to develop platforms enabling Agentic AI for businesses. Despite ongoing efforts to address these challenges, Agentic AI in enterprise settings faces the following obstacles:

• Explainability: In critical processes, it's essential to understand why a particular workflow was generated to solve a problem.
• Supervision: Fully automating workflow execution may not always be feasible. Human oversight is often required to review workflow generation and execution and handle exceptions.
• Ecosystem Complexity: Enterprise environments contain diverse products and solutions, requiring specialized tools and interfaces to create and execute tailored workflows.
• Governance and Security: As with any automation, Agentic AI will need strong governance, privacy, security, and auditing mechanisms to ensure compliance and safety.

Despite these challenges, the continued development of Agentic AI solutions aims to overcome these hurdles and unlock its full potential in enterprise environments.

Watch this YT video on How Agentic AI can change Businesses.

Agentic AI: The AI Agents That Will Change Our Companies E182

Use Cases of Agentic AI

Agentic AI is already creating impact in large enterprises across the world. In a study by McKinsey, Agentic AI was expected to increase automation by at least 20-30% across industries.

From mammoth organizations like Pepsi, to rising challengers like Moneyview, Agentic AI is fast finding a footing in business automation. Here are a few examples of how Agentic AI can dramatically increase productivity -

• Claims processing - A leading Dutch insurer observed up to 90% claim processing automation by using Agentic AI
• Resume screening - Pepsi Co successfully employed AI agents to dramatically increase their speed of screening resumes
• Paralegal assistant - A leading immigration services provider is successfully using Agentic AI to save paralegals time in answering employee queries
• Customer Support - TrueLayer and Moneyview are using Agentic AI to resolve complex L2/L3 tickets across multiple languages

The flexibility of Agentic AI means it can address 100s of use cases across departments and industries. Find some more examples here

Read How Ema Helped Envoy Global:

Envoy Global, a global immigration services provider, faced growing demands to manage the workforce needs of over 1,500 companies. Struggling with lengthy response times, they sought a solution to streamline their legal and support processes. Ema's AI Customer Support Assistant was chosen to automate ticket responses. After seamless integration, it resolved over 50% of support tickets, cutting the workload by 70-80%. This freed up Envoy's legal teams to focus on complex cases, showcasing the efficiency and scalability of Agentic AI. Read more!

What Does The Future Hold For Agentic AI

While using fully autonomous agents may seem like the perfect solution to software sprawl, it’s not that simple. Even if you could generate and execute workflows instantly in an enterprise environment, you still need mechanisms to trace actions, capture relevant data, and ensure proper access for different user groups.

Though Agentic AI is still in its early stages, its functionality and benefits prepare it for quick adoption in enterprise settings. As the technology advances in the coming years, it will be interesting to see how businesses use it to automate day to day work.

Fig: LinkedIn post by Dillion Laird, showcasing an example of Vision Agent determining how full a jar of coffee beans is

Are you a CIO looking to explore how you can start leveraging Agentic AI in your day to day operations?. Let us introduce you to Ema, the Universal AI Employee that is powered by Agentic AI.

Hire Ema as your AI Employee

Ema is the universal AI employee designed to enhance productivity across all organizational roles. Acting as the missing operating system for Agentic AI, she automates complex workflows through simple conversations using the Generative Workflow Engine™. Trusted, secure, and compliant, Ema ensures your data is protected while boosting efficiency.

Powered by EmaFusion™, she integrates over 100 public and private AI models to deliver unmatched accuracy. Ema adapts to any task, making her the perfect fit for streamlining operations, decision-making, and more.

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Transform your organization with Ema’s AI-driven automation. Hire her now and boost your productivity to new levels! Hire her today!