A simple framework for building Agents based on Large Language Models
Moving towards an Agentic Economy
With all the advancements in AI, we are moving towards an agentic economy — a vision of a future economic system where networks of AI intelligent agents work together autonomously to perform user tasks.
What is an Agent?
AI agents are sophisticated software programs and autonomous entities designed to perform tasks for users or other systems. Their ability to interact with their environment, gather data, make decisions based on that data, and execute actions to achieve specific goals is a testament to their intriguing autonomy.
LLM-based Agent concept
According to the paper “The Rise and Potential of Large Language Model-Based Agents: A Survey,” the authors developed a conceptual framework for LLM-based Agents with three components: Brain, Perception, and Action.
Birth of LLM-Based Agents
The framework is versatile and can be customized for different application scenarios. Agents follow a structured workflow, starting with the perception module, which functions similarly to human sensory systems like the eyes and ears. This module perceives changes in the external environment and converts the gathered multimodal information into a comprehensible representation for the agent. Subsequently, the brain module, acting as the control center, engages in information processing activities such as decision-making and operations with storage, including memory and knowledge. Finally, the action module, akin to human limbs, executes tasks with the assistance of tools and impacts the surroundings. Agents can continuously receive feedback and interact with the environment by repeating this process.
My Experimentation —Simple Agent Implementation
Here is a simple implementation of an LLM based con-based conversational agent with this framework in Pe sample images were generated using the “Code Images” tool.
Here is the output after connecting to OpenAI LLM (gpt-3.5-turbo).
Agent Scenarios
The paper's authors also talked about three types of scenarios of LLM-based agent applications—Single Agent, Agent-Agent and Agent-Human.
Scenarios of LLM-based agent applications — The rise and potential of Large Language Model Based Agents: A Survey
A single agent possesses a wide range of capabilities, allowing it to excel in solving diverse tasks across various applications. When multiple agents interact, they can achieve progress through cooperative or adversarial interactions. In human-agent interactions, feedback from humans can significantly enhance the efficiency and safety of agent tasks, while agents can also improve the quality of service they provide to humans.
Considerations for LLM based Agent Development
Define a clear agent profile: Assign a specific role or persona to your agent, including details about its purpose, personality, and expertise. This helps tailor the agent’s behavior to the task at hand.
Implement effective planning mechanisms: Use techniques like Chain of Thought or Tree of Thoughts for task decomposition.
Utilize a robust memory system: Implement a memory module that allows the agent to retain and access relevant information from past interactions, improving context understanding and task performance.
Incorporate diverse evaluation methods: Use a combination of human annotation, Turing tests, specific metrics, and benchmarks to assess the agent’s performance comprehensively.
Narrow the solution space: Define a clear range of possible actions and outcomes to reduce ambiguity and improve consistency in the agent’s performance.
Integrate domain-specific knowledge: Incorporate business heuristics and domain expertise into the agent’s guidance system to enhance decision-making capabilities.
Balance LLM and code-based components: Consider using code-based routers and orchestration where possible to improve reliability and reduce unpredictability in the agent’s behavior.
Address long-term planning challenges: Develop strategies to overcome the limitations of finite context length in LLMs, ensuring the agent can maintain coherence over extended interactions.
The Emergence of Agentic Economy
The emergence of LLM-based agents represents a significant leap forward in AI. The fusion of LLMs’ cognitive abilities with an agent’s capacity to perceive and act opens up a world of possibilities. While there are challenges to overcome, such as ensuring robustness, trustworthiness, and ethical alignment, the ongoing research and development in LLM-based agents hold immense promise for a future where AI empowers humans in unprecedented ways.
Are you ready to build your LLM-based Agent?
By following the best practices, you can create more effective and reliable LLM-based agents using the framework presented in the paper.