Generic

Our Approach

The core concept of agentic AI is the use of AI agents to perform automated tasks with or without human intervention.[1] While robotic process automation (RPA) systems automate rule-based, repetitive tasks with fixed logic, agentic AI adapts and learns from data inputs.

Agentic AI refers to autonomous systems capable of pursuing complex goals with minimal human intervention, often making decisions based on continuous learning and external data. [3] Functioning agents can require various AI techniques, such as natural language processing, machine learning (ML), and computer vision, depending on the environment.

Our Solution

The Model Context Protocol (MCP) is an open standard, open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources.[1] MCP provides a universal interface for reading files, executing functions, and handling contextual prompts.[2] Following its announcement, the protocol was adopted by major AI providers, including OpenAI and Google DeepMind.[

Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents.[1] RPA should not be confused with artificial intelligence as it is based on automation technology following a predefined workflow.[2] It is sometimes referred to as software robotics (not to be confused with robot software).