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Table of Contents
What is. Agentic AI?
- How is Agentic AI Different from RPA?
- Case Studies (Real World Examples)
- What is the Difference Between LLM and Agentic AI?
- Space Inventive's Expertise in Agentic AI
Imagine a world where artificial intelligence not only follows commands but also anticipates needs, makes autonomous decisions, and seamlessly integrates into daily operations. This is the promise of agentic AI, a transformative technology poised to revolutionize industries by enhancing efficiency and innovation. At Space Inventive, we are at the forefront of this evolution, leveraging our expertise to deliver cutting-edge AI solutions that drive business success. Head to our About us page for more info - https://spaceinventive.com/about
Before delving into the intricacies of agentic AI, let's address some of the most frequently asked questions to build a foundational understanding.
What is Agentic AI? - Generative AI fused with Classical Automation
Agentic AI combines classical automation with the power of modern large language models (LLMs), using the latter to simulate human decision-making, analysis and creative content. This means that it can make decisions, take actions, and even learn on its own to achieve specific goals. It’s kind of like having a virtual assistant that can think, reason, and adapt to changing circumstances without needing constant direction.
Explore our Advanced AI technologies - https://spaceinventive.com/datascience
Agentic AI is more than just automation — it has the potential to create a sea change in how we work.
The following set of properties generally define Agentic AI systems:
Generation: Modern Agentic AI systems harness the analytic and creative capacity of LLMs. Unlike simple gen AI apps, however, they don’t simply output a generated text back to the user as a result. Instead, they can use generated outputs as intermediate steps within a complex workflow, mimicking the role of human thought.
Tool Calling: In agentic systems, AI can call upon specific tools or APIs, querying data and triggering events according to the reasoning generated by the LLM.
Discovery: Agentic systems can access real-world data from a variety of tools and data streams, escaping the limitations of their training data. Further, they can harness LLM generation to decide what data they need and to ask for it, rather than being limited to human-provided input, as in retrieval-augmented generation (RAG). For example, an AI agent tasked with maintaining supply chain logistics might write its own queries to weather data APIs and supplier inventory databases, to predict shortfalls and determine possible solutions.
Execution: Agents can take real-world actions, such as interacting with external systems or triggering processes, without human intervention. An AI agent might send emails or other communications to humans, send purchase orders or fund transfers, grant or revoke access to secure systems, or take any action that can be connected to an API.
Autonomy (Self-prompting): Agentic systems are “always on;” they do not need to be triggered to do a specific thing at a specific time, the way a simple chatbot can only respond to a prompt. Instead, once active they can monitor for the right moment to act, relieving humans from this kind of “watch and wait” labor. They can loop through cycles of acting, evaluating and planning, continually ‘self-prompting’ to proceed toward a desired end-state.
Planning: Agentic systems can generate, prioritize and manage sets of subordinate tasks to pursue an overall goal.
Composition: Agentic systems can assemble multiple components—such as queries, scripts or subroutines, calls to APIs or remote functions, into a cohesive action or response. Unlike a script in traditional automation, an AI agent composes a unique solution to a specific problem, using an LLM to reason out how to combine the available resources. This can include delegating work to other AI agents, either by creating them on demand or by communicating across a service boundary.
Memory: Agentic systems can build and maintain their own internal knowledge representations, allowing them to accumulate and utilize information extracted through discovery, and the output of previous actions. This capacity enables agents to function more autonomously, as they can index, store, and retrieve information about the world for use in further tasks. For example, a personal shopper agent for a retail website might maintain an idiosyncratic list of themes and facts about a user extracted from their chat interactions and purchase behavior and use it to customize both conversation and recommendations.
Reflection: Agentic systems can evaluate the solutions they generate and try again, if necessary, rather than delivering low-quality results. For instance, a marketing agent that generates user-customized campaign copy through a multi-step, retrieval-assisted process, might submit all documents to an evaluator AI that predicts the user’s ratings and critical feedback, ensuring that customers only encounter the best possible results.
Explore our Case Studies showcasing real-world AI applications
How is Agentic AI Different from RPA?
While both Agentic AI and Robotic Process Automation (RPA) aim to enhance efficiency through automation, they differ fundamentally:
RPA: Automates repetitive, rule-based tasks by following predefined instructions, lacking the capacity for decision-making or adaptation.
Agentic AI: Exhibits flexibility and decision-making capabilities, enabling it to handle complex, dynamic tasks that require contextual understanding and adaptability.
Scope of automation
- RPA: Focuses on automating highly repetitive, rule-based tasks that mimic human actions. Examples include copying data between systems, generating invoices, processing claims, or assigning permissions when employees are hired or leave an organization. It operates within well-defined boundaries and workflows.
- Agentic AI: Expands beyond task automation to include decision-making, planning, and dynamic interactions. It excels in environments requiring analysis of unstructured data, such as customer support or supply chain optimization. Agentic AI can interpret nuances, learn from interactions, and adapt its behavior over time.
Flexibility and adaptability
- RPA: Operates on scripts and rules, making it rigid in adapting to new or unexpected scenarios. If an input or process deviates from predefined parameters, the system may fail or require manual intervention.
- Agentic AI: Exhibits flexibility by learning from data and interactions. It can analyze complex situations, infer context, and adjust workflows on the fly, making it resilient in dynamic environments. For instance, agentic AI could detect anomalies in a supply chain and recommend alternative routes without prior programming.
Integration and orchestration
- RPA: Integrates with existing systems through APIs or user interfaces but often requires significant setup and maintenance. It performs specific tasks in isolation, with limited orchestration across diverse platforms.
- Agentic AI: Acts as a connective layer across legacy and modern systems, orchestrating processes autonomously. It ensures smooth data flow and efficient operations by making intelligent decisions about which systems to engage and how. For example, an agentic AI system managing customer support could simultaneously pull data from a CRM system, a product database, and an ERP system to resolve complex customer queries.
Decision-making and context awareness
- RPA: Executes tasks based on fixed rules and predefined conditions. It lacks the ability to interpret broader contexts or make decisions beyond its programming.
- Agentic AI: Brings context awareness to automation. It analyzes intent, weighs multiple variables, and makes informed decisions, such as identifying fraud patterns in financial transactions or optimizing energy consumption in a smart grid.
User interaction and autonomy
- RPA: Typically requires human oversight to initiate tasks and address exceptions. Its role is that of a digital assistant, working alongside human operators to enhance productivity.
- Agentic AI: Can operate autonomously or engage users through conversational interfaces like chatbots. It provides a more interactive experience, collaborating with humans or independently performing tasks like conducting customer surveys or troubleshooting IT issues.
User interaction and autonomy
- RPA: Typically requires human oversight to initiate tasks and address exceptions. Its role is that of a digital assistant, working alongside human operators to enhance productivity.
- Agentic AI: Can operate autonomously or engage users through conversational interfaces like chatbots. It provides a more interactive experience, collaborating with humans or independently performing tasks like conducting customer surveys or troubleshooting IT issues.
What is the Difference Between LLM and Agentic AI?
Large Language Models (LLMs) and Agentic AI serve distinct roles:
- LLMs: Excel at understanding and generating human-like text based on vast datasets but do not possess autonomous decision-making abilities.
- Agentic AI: Incorporates LLMs as components, enhancing them with autonomy and the capacity to execute tasks, plan, and strategize independently.
How Does Agentic AI Differ from Traditional AI?
Traditional AI systems are typically designed for specific tasks within fixed parameters, lacking the ability to adapt beyond their programming. In contrast, Agentic AI systems are:
Autonomous: Operate without continuous human oversight.
Adaptive: Learn from interactions and adjust behaviors in real-time.
Goal-Oriented: Pursue objectives across various domains, making them versatile and efficient in complex environments.
Is Agentic AI the Future?
The trajectory of AI development indicates a significant shift towards agentic systems. Their ability to autonomously manage complex tasks, adapt to new information, and operate across diverse environments positions Agentic AI as a cornerstone of future technological advancements. Industries such as healthcare, finance, and logistics are already witnessing the transformative impact of Agentic AI, underscoring its pivotal role in the AI landscape.
Space Inventive's Expertise in Agentic AI
As pioneers in AI, we are committed to delivering scalable, adaptive, and intelligent solutions that redefine possibilities and shape the future of industries. Together, we can leverage the transformative power of AI to solve today’s challenges and embrace tomorrow’s opportunities.
- Customized AI Models: Developing models that address unique challenges with precision and scalability.
- Operational Efficiency: Implementing AI to streamline tasks, reduce costs, and enhance productivity.
- Predictive Analytics: Utilizing advanced machine learning to enable data-driven strategies.
- End-to-End Support: Providing comprehensive services from conceptualization to ongoing optimization, ensuring our solutions evolve with your business.
Partner with Space Inventive to navigate the future of AI, leveraging agentic systems to transform your operations and achieve strategic objectives.
Want to see how Space Inventive’s Agentic AI can transform your business? Explore our services, case studies, or contact us to get started.
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By Amruta Varshini Batteti
Senior Business Development Expert
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