LLM Agents

Unlike traditional LLM applications that generate a single response to a prompt, LLM agents can plan a sequence of actions, interact with APIs, access external knowledge, use software tools, and maintain context across multiple steps. Depending on their design, agents can execute workflows, collaborate with other agents, and adapt their actions based on the results of previous steps. They often combine a language model with components such as memory, planning, tool calling, and orchestration frameworks.

LLM agents enable AI systems to automate complex workflows that extend beyond text generation. They can improve productivity by completing tasks that require reasoning, decision-making, and interaction with external systems. However, because agents can access enterprise data and execute actions, they introduce additional security and governance challenges, including prompt injection, excessive agency, unauthorized tool use, sensitive data exposure, and identity verification. Strong guardrails, access controls, human oversight, and runtime monitoring are essential for secure deployment.