AI agents represent one of the most significant paradigm shifts in artificial intelligence of 2024–2025. Unlike traditional LLM question-answering, agents have autonomous planning capability: given a goal, they can decompose tasks, call external tools, and execute multi-step action sequences until completion. This shift marks AI’s evolution from “answering questions” to “doing work.”
## Four Core Components
**Perception**: agents receive environmental information. Early agents processed primarily text; today’s multimodal agents can read web pages, process images, analyze documents, and “see” screens via computer vision.
**Planning**: this is the core capability that distinguishes agents from ordinary LLMs. Upon receiving a task, agents use Chain-of-Thought reasoning or tree search to decompose complex goals into executable sub-steps. The ReAct (Reasoning + Acting) framework is the dominant planning paradigm: agents alternate between Thought and Action, adjusting plans based on action results.
**Tool use**: agents extend their capabilities by calling external tools — search engines, code executors, database queries, API calls, file system operations. Tool use lets agents access real-time information, execute computations, and interact with external systems. MCP (Model Context Protocol), proposed by Anthropic, is becoming foundational infrastructure for agent ecosystems.
**Memory**: agent memory divides into short-term (dialogue history within the context window), long-term (persistent knowledge in vector databases), and working memory (intermediate state during task execution). Memory system design directly affects coherence across long tasks.
## Major Agent Frameworks
**LangChain / LangGraph**: currently the most complete Python agent framework ecosystem. LangGraph supports state machine structures suitable for complex multi-step agents.
**AutoGen (Microsoft)**: focused on multi-agent conversation frameworks, supporting multiple agent roles (planner, executor, reviewer) collaborating on tasks.
**CrewAI**: role-playing multi-agent framework organizing collaboration through defined role specializations and responsibilities.
**Claude + MCP**: Anthropic’s native agent approach; Claude 3.5/3.7 series performs well on tool calling and long-task execution.
## Current Limitations
AI agents still face significant challenges: error accumulation in long tasks (5% per step compounds to ~40% over ten steps); tool call reliability (hallucinated parameters); and planning quality under ambiguous goals. Despite Gartner projecting 40% of enterprise applications will integrate specialized agents by end of 2026, industrial-grade reliability remains the central research challenge.
See [Multi-Agent System Design](https://sunqi.org/multi-agent-systems-en/) and [MCP Protocol Explained](https://sunqi.org/mcp-protocol-explained-en/).
