Large Language Model (LLM) based agents are increasingly adopted for the automation of complex tasks. In this thesis, I systematically study their capabilities and limitations in cybersecurity forensics. Building upon a publicly available cybersecurity benchmark, I designed and evaluated a modular multi-agent system for forensic analysis. I first addressed two fundamental limitations of LLMs: the lack of long-term memory and the inability to access up-to-date knowledge. To overcome these challenges, I added a semantic memory module for storing and retrieving information and a web search tool (RAG) for external knowledge retrieval. Leveraging these solutions, I then enhanced the agent architecture through iterative refinements.