AI Agents
Autonomous AI systems that perceive their environment, plan multi-step actions, and use tools to complete tasks.
Definition
AI agents combine a large language model with tools (web search, code execution, APIs), memory (conversation history, vector DB), and a planning loop to complete complex multi-step tasks with minimal human intervention. Unlike chatbots that answer individual queries, agents execute workflows that span many actions over time.
Agent architectures include ReAct (interleaving reasoning and actions), chain-of-thought with tool use, and multi-agent systems where specialised agents collaborate. Frameworks like LangChain, AutoGen, CrewAI, and LlamaIndex provide scaffolding. Evaluation is challenging: agents may complete tasks in unexpected ways or fail silently.
Enterprise use cases include software development automation (Devin), research assistance, email and calendar management, and workflow orchestration. Safety considerations include sandboxed execution environments, human approval gates for high-stakes actions, and audit trails.
Examples
- Devin (Cognition AI)
- ChatGPT Plugins
- AutoGPT
- CrewAI
- Microsoft Copilot agents
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Related Terms
Large Language Model (LLM)
A transformer-based AI system trained on billions of tokens of text, capable of generating, reasoning about, and transforming language.
RAG (Retrieval-Augmented Generation)
Grounding LLM responses by first retrieving relevant documents from a knowledge base before generating an answer.
Reinforcement Learning (RL)
A learning paradigm where an agent learns by taking actions in an environment and receiving reward signals.