Editing AI Agents and Agentic Systems (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
The core insight behind agents is that '''LLMs can serve as cognitive engines for sequential decision-making'''. When given tools and a reasoning framework, a capable LLM can decompose complex tasks, decide which tools to use, interpret results, and adapt its plan — all expressed in natural language.

The ReAct pattern captures this loop explicitly:
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Thought: I need to find the current population of Tokyo.
Action: web_search("current population of Tokyo 2024")
Observation: Tokyo's population is approximately 13.96 million (2024).
Thought: I have the information. I can now answer the question.
Final Answer: Tokyo's population is approximately 13.96 million as of 2024.
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This is powerful because it makes the agent's reasoning transparent and interruptible — a human can read the trace and understand exactly what the agent was thinking.

'''Memory architecture''' is crucial for long-horizon tasks:
* '''In-context memory''': Everything in the current prompt window. Finite and expensive.
* '''External memory''': A vector database or key-value store the agent can read/write. Enables persistence across sessions.
* '''Episodic memory''': A log of past successful task completions that can be retrieved to guide similar future tasks.

'''Multi-agent systems''' divide labor among specialized agents — a researcher agent, a coder agent, a critic agent — coordinated by an orchestrator. This mirrors how human organizations work: different specialists with defined interfaces.

The '''agent loop''' is not guaranteed to terminate. Agents can get stuck, hallucinate tool calls, or enter cycles. Robust implementations require termination conditions, step budgets, and error handling.
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