Agents, Tools, and Agent Frameworks

Modern AI increasingly acts as agents that plan, use tools, browse, and remember. This sub-article expands Section 2.5 of Hardware & Software and Recent Topics.

What Is an Agent?

An agent perceives, decides, and acts toward goals. In current practice, an LLM is wrapped in a loop that:

• Observes the environment (text, APIs, screens, sensors).
• Reasons about next steps.
• Calls tools (search, code execution, calculators).
• Updates memory.

Frameworks and Libraries

• LangChain, LlamaIndex, and AutoGPT-style stacks orchestrate agent loops.
• OpenAI Functions/tool-calling and Anthropic’s computer use let models invoke tools natively.

Reasoning Patterns

• Chain-of-thought and ReAct (Reason+Act) interleave thinking and acting.
• Tree-of-thought explores multiple reasoning branches.

Memory

• Vector databases (Pinecone, Weaviate, FAISS) store embeddings for long-term recall.
• Conversation history, scratchpads, and external knowledge bases compose agent memory.

Why It Matters

Agents connect models to the world, enabling automation, research, and real-world action—and raising new safety questions.

Sources

• Yao, S. et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models.
• Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Back to Hardware & Software · See Agents & Autonomy.