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Agents & Agent Flows
Last updated
Last updated
LLMs have emerged as entities capable of processing and generating human-like text with astonishing accuracy and fluency. However, the potential of these models extends beyond mere content generation; they can be harnessed as agents within multi-agent systems, paving the way for collaborative problem-solving, dynamic interactions, and sophisticated decision-making.
An agent is an autonomous unit programmed to perform tasks, make decisions, and communicate with the environment. LLM is best at understanding and generating content. However, they lack access to the outside environment and depend only on information it has been trained on. For our current use case, we will need to write some additional code to fetch the weather information and then use the LLM to generate the email content. And then write some more code to send the email to the user. This is where Agents come in. They can interact with the outside world, fetch information, and then use the LLM to generate content. They can also take actions based on the generated content, like sending an email to the user.
Agents are sophisticated systems that leverage Large Language Models (LLMs) to function autonomously. These agents can:
Reason and Make Decisions: They analyze data and context to make informed decisions without constant human oversight.
Perform Tasks: Agents can execute a wide range of tasks, from simple data retrieval to complex multi-step operations.
Learn and Adapt: By utilizing memory and advanced algorithms, agents can learn from past interactions and adapt to new situations.
An LLM-Based Multi Agent System consists of multiple agents that work together to achieve a common goal. Each agent in the system has a specific role and specialized in performing a particular task. Each has access to different tools, can make decisions and communicate with other agents in the system.
The idea behind a multi-agent system is to bring a sense of teamwork. Each agent with a different role and context with a different perspective can work together to achieve a common goal.
The benefits of using Multi-Agent Systems are:
Single Responsibility Principle — Each agent has a specific role and is responsible for performing a particular task. This ensures that each agent is focused on its task and does it well.
Breakdown of Complex Tasks — Complex tasks can be broken down into smaller tasks and assigned to different agents. This makes it easier to manage and execute complex tasks.
Enhanced Collaboration — Agents can communicate with each other and share information. This enables them to work together and achieve a common goal.
Scalability — The system can be easily scaled by adding more agents to perform additional tasks. This makes it easy to adapt to changing requirements and scale the system as needed.
Fault Tolerance — If one agent fails to give the expected output, there exists a chance that the next agent will ask the previous agent to run again to generate the answer. Thus, the system drives towards fault tolerance. Also, they can delegate the tasks to another agent if required.
Enhanced Performance — Agents can work in parallel to perform tasks. This can lead to faster execution of tasks and improved performance of the system.
Reusability — Agents can be reused in different systems to perform similar tasks. This makes it easy to reuse existing agents and build new systems quickly.
Memory Storage and Retrieval: They can store and access data from a built-in knowledge graph.
Function Calling: Agents can perform specific functions or tasks as defined by the user.
Web Search and Data Scraping: They can retrieve up-to-date information from the internet to enhance their operations.
API Integration: Agents can interact with external services using personal API keys, enabling a broad range of applications.
These agents operate within a Trusted Execution Environment (TEE) and have access to a Python interpreter for executing generated code. They are modular, meaning different agents can be connected to create complex workflows.
