Robotic Process Automation (RPA)
Software that automates repetitive, rule-based tasks by mimicking human interactions with digital systems like clicking, typing, and copying data between applications. AI-enhanced RPA adds intelligence to handle exceptions and unstructured data.
Robotic Process Automation emerged as a way to automate legacy workflows without modifying underlying systems. Traditional RPA uses scripted bots that follow deterministic rules: click this button, copy that field, paste it there. AI-enhanced RPA adds language models to handle variability, making bots more resilient to UI changes and capable of processing unstructured inputs like emails or documents.
For organizations with established toolchains, RPA bridges the gap between manual processes and full API integration. Growth teams often use RPA to automate data entry between marketing platforms, synchronize customer records across systems, or generate reports from tools that lack APIs. The convergence of RPA with AI agents is a significant trend: instead of brittle scripts, AI-powered RPA bots can adapt to interface changes, handle edge cases, and make judgment calls. However, RPA should be viewed as a transitional technology. Where possible, invest in proper API integrations for reliability and maintainability.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.