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  "documentTitle": "Rise of Agentic AI Report",
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      "text": "Although AI agents could be set up to operate autonomously, they still function within the scope of execution. Critical situations will require human oversight or decision-making.",
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      "text": "A AI agents interact with their environment and receive triggers, inputs, or goals from users.\nB By employing reasoning models, they can break down the goals into specific actions or steps and prioritize them.\nC For task execution, AI agents may access internal data and enterprise tools systems (such as knowledge bases and customer relationship management [CRM]), external tools (such as web search, third-party databases), and interact with other AI agents, or request clarification from users.\nD In a multi-agent system, an “orchestrator” agent can break down the larger problem into smaller pieces to be tackled using agents that leverage small language models, significantly reducing the cost and time needed to arrive at an outcome. Guardrails enforce ethical, operational, and safety standards.\nE The above iterations can be repeated in different combinations and until the stated goals have been achieved. The agent utilizes its memory to maintain context, learn from previous iterations or past experiences, and improve its performance over time.\nF Agent-to-Agent (A2A) protocols help collaboration between agents, while Model Context Protocol (MCP) aids in accessing external tools. MIT’s Project NANDA is an initiative to develop the foundations of the Internet of AI Agents.",
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      "text": "Although AI agents could be set up to operate autonomously, they still function within the scope of execution. Critical situations will require human oversight or decision-making.\nFor more details on these terms, please see the glossary in the appendix.",
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      "text": "How do AI agents work?",
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