AI Agent
Autonomous software that analyzes, decides, and acts using reasoning and tool-calling.
Autonomous Agent
Operates without human supervision with goal-directed behavior.
Workflow Agent
Executes multi-step business processes end-to-end.
RAG Agent
Combines search and reasoning for more accurate results.
Tool-Calling
Mechanism allowing agents to use APIs, apps, or plugins.
Orchestration
Coordination of multiple agents or tools in a workflow.
MCP (Multi-Agent Communication Protocol)
Standard for agent interoperability and collaboration.
Multi-Agent System
Multiple agents working together to complete tasks.
Reactive Agent
Responds to specific triggers with predefined actions.
Planning-Based Agent
Creates execution strategies and adapts to changing conditions.
Tool-Using Agent
Accesses and manipulates external tools and data sources.
Agent Architecture
Technical framework defining agent capabilities and behavior.
Interoperability
Ability of different agents and systems to work together.
State Management
Handling of persistent information across agent interactions.
Fault Tolerance
Ability to continue operating despite failures or errors.
Governance Controls
Mechanisms for managing agent behavior and compliance.
Ethical AI
Responsible AI practices ensuring fairness and accountability.
Zero-Trust Security
Security model requiring continuous verification of all access.
Edge Computing
Processing data near its source for reduced latency.
Predictive Maintenance
Proactive system monitoring and maintenance prediction.
Cross-Platform Mobility
Ability to operate across different computing environments.
API Integration
Connection mechanisms for accessing external services.
Webhook Support
Real-time notifications for triggering agent actions.
Natural Language Processing
Technology for understanding and generating human language.
Machine Learning
Algorithms that improve performance through data analysis.
Deep Learning
Advanced ML using neural networks for complex pattern recognition.
Reinforcement Learning
Learning through trial and error with reward systems.
Supervised Learning
Training on labeled data for prediction tasks.
Unsupervised Learning
Finding patterns in unlabeled data.
Transfer Learning
Applying knowledge from one domain to another.
Few-Shot Learning
Learning from minimal examples or data.
Zero-Shot Learning
Performing tasks without specific training examples.
Context Awareness
Understanding situational factors in decision-making.
Sentiment Analysis
Determining emotional tone from text or communications.
Named Entity Recognition
Identifying and classifying named entities in text.
Intent Classification
Determining user intentions from natural language input.
Dialog Management
Handling conversational flow and context.
Knowledge Graph
Structured representation of information and relationships.
Ontology
Formal representation of concepts and their relationships.
Semantic Web
Framework for sharing data across applications.
Linked Data
Method of publishing structured data for linking.
Schema Markup
Structured data markup for search engines.
JSON-LD
JavaScript Object Notation for Linking Data format.
Microdata
HTML specification for nested structured data.
RDF (Resource Description Framework)
Standard for data interchange on the Web.
OWL (Web Ontology Language)
Ontology language for the Semantic Web.
SPARQL
Query language for RDF data.
Knowledge Base
Structured repository of information.
Inference Engine
Component that derives new knowledge from existing facts.
Reasoning Engine
System for automated logical reasoning.
Decision Tree
Model for decision-making using tree-like structures.
Neural Network
Computing system inspired by biological neural networks.
Convolutional Neural Network
Specialized for processing grid-like data.
Recurrent Neural Network
Designed for sequential data processing.
Transformer Architecture
Attention-based model for sequence processing.
Large Language Model
Advanced language processing system trained on vast text data.