Autonomous AI systems that can plan, execute tasks, and use tools to achieve goals.
AI Agents are autonomous systems that combine language models with the ability to take actions, use tools, and make decisions to accomplish complex goals. Unlike simple chatbots that respond to single queries, agents can break down tasks, plan multi-step approaches, execute actions, observe results, and iterate until objectives are achieved.
Key components of AI agents include: Planning (decomposing goals into subtasks), Memory (maintaining context across steps), Tool Use (calling APIs, running code, searching), and Reflection (evaluating progress and adjusting strategies). Frameworks like AutoGPT, BabyAGI, and LangChain's agent modules provide foundations for building agents.
Building reliable agents is challenging due to compounding errors, context management, cost control, and safety considerations. Modern approaches focus on well-defined tool sets, clear task boundaries, human-in-the-loop oversight, and robust error handling. Understanding agent architectures is essential for engineers building sophisticated AI-powered automation.
Autonomous AI systems that can plan, execute tasks, and use tools to achieve goals.
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