AiVibeAgentic AI
Agentic AI describes AI systems designed to act with a degree of autonomy towards goals, planning and executing multi-step tasks with tools and adapting to the results, as opposed to systems that only generate a response to each prompt.
Agentic systems combine a capable model with planning, tool use, memory and feedback loops. They may decompose a goal into subtasks, choose tools, check their own outputs, retry on failure and hand work to other agents or to people. The degree of autonomy varies from assistants that suggest each action for approval to systems that complete whole workflows unattended.
Organisations apply agentic AI to software development, IT operations, customer service, research and data analysis, and back-office processes such as procurement and reporting. In operational technology, interest centres on assistants that diagnose issues and prepare actions for people to approve.
More autonomy means more risk: errors compound over many steps, costs can grow unpredictably and agents may act on manipulated inputs. Governance defines what agents may do, which actions need human confirmation, how actions are logged and how agents are evaluated and monitored. The OWASP GenAI Security Project has published guidance on threats specific to agentic AI.
Key points
- AI systems that plan and act autonomously towards goals
- Combines models with tools, memory and feedback loops
- Autonomy ranges from approving each step to unattended workflows
- Requires governance of permissions, approvals and logging
Where AiVibe comes in
In the AiAmbA AI Factory, AI agents only propose changes; a trained operator confirms each one within limits a verified AiAmbA engineer commissioned.
Related terms
- AI AgentAI & Machine Learning
- Multi-Agent SystemAI & Machine Learning
- Function Calling (Tool Use)AI & Machine Learning
- AI GuardrailsAI & Machine Learning
- Responsible AIAI & Machine Learning