Acting
“Acting” in the context of agentic-ai refers to the capability of an autonomous system to execute actions based on reasoning and environmental feedback, distinguishing it from static text generation. This concept encompasses behavioral frameworks, tool usage, and adaptive decision-making loops.
Core Components
- ReAct Framework: A paradigm combining reasoning (chain-of-thought) and acting (tool usage). The agent generates a thought process, determines an action to take, observes the result, and iterates until a goal is met AI Agents Explained: ReAct Framework, Behavioral Types, and Google ADK.
- Behavioral Types: AI agents exhibit distinct behavioral patterns based on their configuration, ranging from simple reactive scripts to complex, planning-oriented systems.
- Google ADK (Agent Development Kit): A toolkit for building production-grade agents, emphasizing modular design and integration with cloud infrastructure AI Agents Explained: ReAct Framework, Behavioral Types, and Google ADK.
Key Distinctions
- Agents vs. Chatbots: Traditional chatbots rely on pre-defined responses or static pattern matching. AI agents possess agency; they can plan sequences of actions, use external tools (APIs, databases), and adapt to unexpected outcomes AI Agents Explained: ReAct Framework, Behavioral Types, and Google ADK.
- Reasoning Loop: The core of “acting” is the cycle of Thought → Action → Observation. This loop allows the agent to refine its approach in real-time.