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How does self-correction work in agentic AI?

Self-correction in agentic AI is the ability of an agent to detect when a step has failed or produced an unexpected result, diagnose the problem, and adjust its approach so it can continue toward the goal without requiring a human to restart the process. Key Takeaways...

What are the four core capabilities of agentic AI?

The four core capabilities of agentic AI are persistent memory, tool use, planning and goal decomposition, and self-correction. Together they enable an AI system to maintain context, take real actions, break complex goals into steps, and recover from errors without...

What is persistent memory in agentic AI systems?

Persistent memory in agentic AI systems is the ability of an agent to retain, retrieve, and update relevant information across multiple interactions and sessions so it does not start from zero every time it is invoked. Key Takeaways Persistent memory gives agents...

What is planning and goal decomposition in agentic AI?

Planning and goal decomposition in agentic AI is the process by which an agent takes a high-level objective, breaks it into ordered subtasks, identifies dependencies and required tools, and creates an executable sequence that it can adjust as conditions change. Key...

How do AI agents use tools / What is tool use in agentic AI?

Tool use in agentic AI is the capability that lets an AI agent call external systems, APIs, databases, code environments, and business applications so it can gather live information and take real actions instead of only generating text. Key Takeaways Tool use turns a...