NemoClaw Knowledge Wiki

agentic systems

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🗂️ AI & Agents · View mindmap

Agentic systems represent a shift in artificial intelligence architecture from passive language models that respond to queries toward integrated systems capable of autonomous goal-directed behavior. Rather than simply generating text in response to user input, agentic systems are designed to plan tasks, take actions, and iterate toward objectives with minimal human intervention. This architectural evolution reflects growing capabilities in language models and the recognition that many real-world problems require sequential decision-making and tool interaction rather than single-turn responses.

Core Characteristics

The defining features of agentic systems include the ability to decompose complex objectives into subtasks, select and execute appropriate tools or actions, evaluate outcomes, and adjust strategies based on results. These systems typically operate through iterative loops where a language model assesses the current state, determines next steps, and reasons about whether goals have been achieved. Key components often include planning mechanisms, memory systems to track context across multiple steps, and interfaces to external tools, APIs, or computational resources.

Current Development

Recent work by organizations including Anthropic has focused on making these systems more reliable and interpretable. Anthropic’s research has explored how language models can be prompted to engage in extended reasoning, use tools effectively, and handle errors gracefully. The field remains nascent, with ongoing investigation into how to ensure agentic systems remain controllable, pursue intended objectives accurately, and avoid unintended behaviors as they operate with greater autonomy.

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