title: "LLM Agents: An Overview" date: "2026-03-29" description: "A survey of large language model agent architectures, from tool use to multi-agent systems." tags: ["LLM", "agents", "AI"] type: research topic: "LLM Agents" aiModel: "claude-opus-4-6" draft: false
Introduction
Large language model (LLM) agents represent a significant evolution beyond simple chat interfaces. Rather than generating a single response, agents can plan, use tools, and execute multi-step tasks autonomously.
Core Architecture
An LLM agent typically consists of:
- The model — A capable LLM serving as the reasoning engine
- Tool definitions — A set of callable functions (search, code execution, file I/O)
- Memory — Short-term context window plus optional long-term retrieval
- Orchestration loop — The cycle of observe → think → act
Tool Use
Tool use (also called function calling) allows models to invoke external APIs and return structured results. This transforms the model from a text generator into an active participant in computational workflows.
def search_web(query: str) -> list[dict]:
"""Search the web and return results."""
...Multi-Agent Systems
Multiple specialized agents can collaborate, with each agent handling a specific domain. A planning agent decomposes tasks, execution agents carry them out, and a verification agent checks results.
Challenges
- Reliability: Agents can hallucinate tool calls or get stuck in loops
- Cost: Multi-step tasks consume many tokens
- Safety: Autonomous agents need careful sandboxing
Conclusion
LLM agents are rapidly maturing. The combination of capable models, reliable tool use, and robust orchestration frameworks is making genuinely useful autonomous AI systems possible.