Contrasting RPA, Chatbots & AI Agents
…and the Rise of Agentic Capabilities in Automation
Introduction
Automation in business and consumer applications is evolving fast, blurring lines across various technologies that offer distinct but overlapping capabilities.
Established tools like Robotic Process Automation (RPA), chatbot build frameworks and AI Agents are being integrated in new ways with Agentic Capabilities to solve increasingly complex problems.
In many cases applications incorporate features from multiple automation approaches.
This post isn’t about drawing hard boundaries between these technologies.
Instead, in the image below, I wanted to look at how they differ from a traditional approach in terms of strengths and challenges, especially with the emergence of agentic capabilities that add a new layer of flexibility and autonomy to software tools.
The Rise of Agentic Capabilities
A whole host of application types are now integrating agentic capabilities, allowing software to act with a degree of autonomy.
These agentic systems don’t just follow preset rules but can make real-time adjustments, interpreting complex input and taking actions that best align with the given task.
Large tech companies, including Microsoft, Salesforce, IBM and others are racing to introduce agent functionalities, aiming to offer solutions that respond dynamically and provide greater operational flexibility.
Beyond standalone AI Agent solutions, we also see existing automation platforms infusing agentic capabilities to enhance their adaptability and broaden their utility.
What Defines an AI Agent?
At its core, an AI Agent is a piece of software supported by language models, typically large language models (LLMs), which allow it to handle complex queries and tasks.
Unlike traditional automation tools, an AI Agent can decompose a problem into a sequence of steps and handle each step individually.
Through and iterative processes of Thought, Action, Observation, etc, the agent moves towards a solution while adjusting its actions based on immediate feedback.
AI Agents also leverage tools that allow them to interact with various systems, from APIs to web searches, depending on the task requirements.
The scope and diversity of these tools determine the “power” or effectiveness of the agent, allowing it to respond intelligently to diverse queries and execute complex workflows.
Diving into Each Approach
Robotic Process Automation (RPA)
Advantages: RPA is great for handling repetitive, rule-based tasks like data entry and processing in HR or finance. By removing manual effort, it speeds up workflows, reduces errors and increases efficiency.
Challenges: RPA is less flexible when workflows need dynamic decision-making or frequent updates. Once set, RPAs don’t adjust well, so updates need manual reconfiguration, which can limit their applicability in rapidly changing environments.
Chatbot Flows
Advantages: Chatbots offer a structured approach to common customer queries, guiding users through predefined paths that are easy to set up and effective for FAQs or appointment scheduling.
Challenges: The rigidity of chatbot flows can be frustrating for users with more complex or unique needs. As they’re confined to pre-scripted responses, they’re often limited in how they handle unexpected inputs or intricate problems.
AI Agents (Dynamic Flow Creation)
Advantages: AI agents introduce a new level of adaptability and autonomy, making them ideal for tasks requiring a deeper understanding or handling unexpected inputs.
With the ability to create and adjust flows in real-time, they offer personalised responses and greater flexibility, making them suited to multi-step processes and complex troubleshooting.
Challenges: The complexity of AI agents can also be their downside. They typically require more resources to manage, and their access to multiple tools and integrations can make oversight challenging.
Final Thoughts
Agentic activity in AI-driven applications is advancing rapidly, with three primary streams emerging:
Native AI Agent Frameworks
Native AI Agents represent the purest form of agent technology, where systems are designed from the ground up to operate independently, leveraging large language models and specialised architectures to take action without constant human guidance.
These frameworks are inherently agentic, built with the capability to interact across multiple platforms, autonomously execute tasks, and make decisions based on real-time data. OpenAI’s GPT-4 with tools, Anthropic’s AI agent offerings, Kore.ai’s GALE and frameworks like LangChain exemplify this category by focusing on robust, complex chains of actions that adapt dynamically to user needs and environmental cues.
Enhanced Chatbot and RPA Systems with Agentic Capabilities
Traditional automation technologies, such as chatbots and robotic process automation (RPA), are increasingly incorporating agentic features.
Initially designed for rule-based, repetitive tasks (RPA) or structured conversational flows (chatbots), these systems are now adding layers of dynamic interaction that enable more flexible responses.
This evolution expands the scope of both RPA and chatbot frameworks to handle more complex, less predictable workflows.
General Applications Integrating Agentic Discovery and Interaction
In addition to purpose-built AI Agents and enhanced automation tools, general-purpose applications are beginning to integrate agentic discovery and interaction functionalities.
Consider here the work Microsoft is doing to introduce agentic capabilities to Windows and Apple with their Ferret-UI research for iOS.
In these streams, agentic functionality provides a spectrum of autonomy and complexity, allowing businesses to choose the right level of intelligent assistance for their needs.
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Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
