AI agents have emerged as a significant topic in artificial intelligence, capable of autonomously performing tasks such as making online purchases, developing software, researching trends, or booking travel arrangements. This advancement transitions generative AI beyond simple chat interfaces, enhancing its operational capabilities.
Recent developments have seen rapid progress in agentic AI, particularly with the introduction of key components like the model context protocol (MCP), which is just a year old. Diverse interpretations and definitions of what constitutes an AI agent exist, making the landscape complex.
In its essence, an AI agent can be defined as a system that utilizes a large language model (LLM) to achieve certain objectives. For instance, if tasked with reserving a restaurant table, the model would use available tools, such as databases of restaurants, to achieve this goal through a series of planned actions.
To implement this functionality, several foundational elements are necessary:
- Agent Development: Various frameworks exist to streamline the agent-building process.
- Execution Environment: Running an LLM often requires specialized hardware and expertise.
- Agentic Code: Users develop code that defines the agent’s functions, typically executed in cloud environments to ensure consistent performance.
- Communication Mechanism: A protocol is needed for effective interaction between the LLM and external tools.
- Memory Systems: Short-term memory tracks interactions during a session, while long-term memory stores user preferences for future interactions.
- Execution Tracing: Tools are needed to monitor and evaluate agent performance.
Current methodologies for developing agents typically rely on the ReAct model, emphasizing a thought-action-observation cycle. The usage of APIs and potential for automated code generation allows for enhanced flexibility in the tasks agents can manage.
Furthermore, as agents often require authorization to access resources on behalf of users, different approaches exist, including OAuth for secure credential handling. Observability tools provide insights into agent performance and user satisfaction, marking an evolution in monitoring software systems.
Source: https://venturebeat.com/ai/under-the-hood-of-ai-agents-a-technical-guide-to-the-next-frontier-of-gen-ai