The term "Agentic AI" is increasingly showing up in technology conversations, and for good reason. Agentic AI refers to autonomous AI-powered agents capable of performing complex, independent tasks by integrating with multiple systems and tools. Unlike traditional software bots or automation scripts, these AI agents can reason, understand context, make decisions, and carry out actions independently.
Agentic AI utilizes large language models (LLMs), like those powering ChatGPT, Claude, or Google's Gemini, combined with APIs and enterprise data sources. This blend allows these agents to execute tasks autonomously—from scheduling meetings and responding to emails to managing security threats and automating sales workflows.
Why is Agentic AI critical today? Organizations are under increasing pressure to optimize efficiency and productivity. AI agents significantly reduce manual workload, allowing human employees to focus on strategic, higher-value tasks. As businesses look for new competitive advantages, adopting Agentic AI solutions can drive innovation, speed up processes, and reduce human error.
However, the autonomy of agentic AI brings new challenges. Organizations must ensure that these powerful tools operate securely, transparently, and in compliance with regulatory requirements. Effective governance frameworks, clear usage policies, and continuous monitoring are essential for responsible deployment.
The rise of Agentic AI signals the next wave of business automation. Companies that embrace and responsibly manage this technology will find themselves ahead in a rapidly evolving digital landscape.
Agentic AI Essentials: Key Terms Every Business Leader Should Know
Navigating the rapidly evolving landscape of Agentic AI requires a solid understanding of foundational terms and concepts. Whether you're considering adopting AI solutions or simply staying informed, here's an essential glossary every business leader should know.
Agentic AI: Autonomous AI-powered agents capable of independent decision-making and performing complex tasks by interacting dynamically with systems and data.
Large Language Models (LLMs): Powerful AI systems trained on vast amounts of text data, capable of generating human-like responses and understanding natural language queries. Examples include GPT (OpenAI), Claude (Anthropic), and Gemini (Google).
Non-Human Identities (NHIs): Digital entities such as service accounts, bots, and AI agents that perform automated actions and require governance to manage access, privileges, and security.
Model Context Protocol (MCP): A standardized framework designed to securely manage and share contextual information between AI agents and enterprise systems, enabling safer and more effective integrations.
Prompt Engineering: The process of crafting effective prompts to guide an LLM's responses, enhancing the accuracy, relevance, and reliability of the AI agent's outputs.
Fine-Grained Authorization: Security practices that involve granting precise, minimal permissions to AI agents and NHIs, limiting their access strictly to what is necessary to perform their tasks.
API (Application Programming Interface): Protocols that allow AI agents to communicate with software systems, enabling them to execute tasks across various platforms and tools within an enterprise.
Governance & Compliance: The frameworks and processes put in place to ensure AI agents operate securely, transparently, and within regulatory guidelines.
Understanding these foundational terms positions you to effectively evaluate, implement, and manage Agentic AI technologies within your organization.
About Natoma
Natoma enables enterprises to adopt AI agents securely. The secure agent access gateway empowers organizations to unlock the full power of AI, by connecting agents to their tools and data without compromising security.
Leveraging a hosted MCP platform, Natoma provides enterprise-grade authentication, fine-grained authorization, and governance for AI agents with flexible deployment models and out-of-the-box support for 100+ pre-built MCP servers.
