Glossary
A list of core Agentic AI terms and definitions.
1. Agentic AI
Agentic AI refers to artificial intelligence systems that can plan, act, and adapt toward defined goals with minimal human input.
Unlike traditional automations that only respond to prompts, agentic systems use reasoning, memory, and autonomous action to perform multi-step tasks — such as analysing data, making decisions, and executing workflows across business tools.
2. AI Agents
An AI Agent is a software entity powered by AI models that can observe, decide, and act within a digital environment.
Agents can access data, call APIs, and perform tasks on behalf of users — from drafting emails to managing full operational processes.
They often use frameworks like LangChain, CrewAI, or OpenDevin to coordinate reasoning, memory, and task execution.
3. Automations
Automations are rule-based workflows that trigger predefined actions when certain conditions are met.
They improve efficiency by removing manual steps — for example, sending confirmation emails when a form is submitted or updating a CRM when a lead changes status.
Automations are fast and reliable, but limited to what they’ve been explicitly programmed to do.
4. AI Automations
AI Automations combine traditional workflow logic with artificial intelligence capabilities such as text generation, data interpretation, and decision-making.
They can handle variability and context — for example, automatically categorising incoming messages, summarising customer feedback, or adapting outputs to tone and intent.
AI Automations bridge the gap between static logic and dynamic reasoning.
5. Autonomous Agents
Autonomous Agents are advanced AI systems capable of taking initiative, setting intermediate goals, and self-directing their own actions to achieve an overall objective.
They operate continuously, monitor outcomes, and learn from feedback.
This autonomy makes them suitable for long-running or multi-system tasks, such as financial analysis, research synthesis, or project coordination.
6. Multi-Agent Systems (MAS)
Each agent may specialise in a different function — such as data retrieval, analysis, or execution — and they coordinate via shared protocols.
MAS designs are becoming common in enterprise applications, where distributed intelligence mirrors real-world team dynamics.
7. Agent Framework
An Agent Framework provides the architecture for building, deploying, and managing AI agents.
It defines how an agent perceives information, reasons about tasks, and interacts with external systems.
Popular examples include LangChain, AutoGen, CrewAI, and OpenDevin, each offering distinct approaches to orchestration, tool use, and memory management.
8. Responsible AI
Responsible AI is a framework for developing and deploying AI systems that are ethical, transparent, and accountable.
It covers governance, bias reduction, data protection, and human oversight.
At Brand Agentic AI, all projects are built under Responsible AI principles to ensure safety, compliance, and trust.
9. Workflow Integration
Workflow Integration is the process of connecting AI agents or automations with existing business systems — such as CRMs, project tools, or databases — so that data flows seamlessly between them.
It ensures that new AI capabilities enhance rather than disrupt existing operations.
10. Generative AI
Generative AI describes models capable of producing new content — text, images, code, or audio — based on learned patterns.
While generative models are often embedded within agentic systems, agentic AI goes further by using that generative intelligence to plan and act in pursuit of defined goals.