AI Agents and Tool Use LLM is frameworks enabling language models to autonomously select and invoke external tools (APIs, calculations, search) within an iterative loop for complex task solving — extends LLM capabilities beyond text generation. Agents perform reasoning and planning. Agent Loop and Reasoning agent receives task, reasons about solution strategy, selects tool, executes, observes result, repeats until completion. Multi-turn interaction enabling complex problem-solving. Explicit reasoning steps improve transparency and error correction. Tool Definition and Specification tools defined as functions with signatures: name, description, parameters. LLM selects appropriate tool given task. Descriptions critical for correct tool selection. Function Calling LLM outputs structured function call (tool_name, arguments). Model interprets output, executes function, returns result. Two approaches: structured output generation (ensure valid JSON/XML), special tokens for function calls. Planning and Task Decomposition LLM breaks complex tasks into subtasks, plans execution order. Examples: web search for information, calculator for arithmetic, Python for programming. Hierarchical planning: high-level plan decomposed recursively. Web Search and Information Retrieval tool enabling agent to search internet, retrieve current information. Solves knowledge cutoff problem. Code Execution Environment sandbox for executing Python code. Agent writes code, observes output, refines. Enables exact computation (unlike numerical generation). Reasoning Prompting techniques like chain-of-thought improve tool selection. "Think step by step" prompts agent to reason before acting. Error Recovery and Retry tools fail or return unexpected results. Agent observes error, reasons about cause, retries with adjusted approach. Fault tolerance essential. Knowledge Base Integration tool accessing knowledge bases, databases, documents. Retrieval-augmented generation: agent searches knowledge base, grounds responses in retrieved information. Memory and Context Management agent maintains conversation history, extracted knowledge. Long-term memory enables continuity across multiple sessions. Tool Composition tools combined: search finds information, calculator computes, code writes summary. Complex workflows emerging from simple tools. Evaluation and Reliability test agents on benchmark tasks requiring tools. Measures: task completion, tool accuracy, reasoning quality. Agent Hallucination agent may fabricate tool outputs or misuse tools. Mitigated via grounding in actual tool execution. Real-World Applications customer service agents (search knowledge base, contact systems), research assistants (search literature, synthesize), software engineering (code search, generation, execution). Prompt Engineering detailed tool specifications, clear examples critical for effective tool use. Few-shot prompting teaches tool selection patterns. Safety and Constraints tools can have dangerous capabilities. Sandboxing, permission systems, rate limiting prevent abuse. Agent Frameworks LangChain, AutoGPT, ReAct enable tool-using agents with different reasoning paradigms. AI agents leveraging tools transcend pure language limitations enabling complex, real-world task solving.