Agent Studio
Last updated
Last updated
Creating an agentic workflow on the platform involves a series of simple steps that transform your high-level goals into a functioning AI system capable of performing complex tasks on your behalf.
Begin by chatting with the platform’s chatbot to define what kind of agent you want. Simply tell the bot what you need, including any specific instructions or requirements for the agent. The information provided will be used to generate a blueprint, which may include goals, data sources, tools, APIs, and suggested LLMs or agents. These elements can be adjusted as needed, and you also have the flexibility to add more tools, APIs, or data sources if required. This interactive approach makes it easier for you to articulate the agent's goals in a conversational manner.
Your request can include tasks such as data analysis of a project, optimizing a code repository, content generation, and much more. It is truly open-ended and depends on your ability to instruct the system. The platform will use this information to determine the necessary tools and memory required to generate agent workflows.
Once your requirements are specified, a blueprint of the workflow will be automatically generated with optimal parameters. Examine this blueprint to understand the proposed workflow and identify any changes or adjustments that may be needed. The blueprint provides a visual interface of how the agent will operate, including the sequence of tasks and the tools to be used. This visualization helps in understanding and optimizing the agent’s behavior.
Note: While the auto-generated blueprint is robust, there is a chance that LLM hallucinations may lead to inaccuracies or suboptimal configurations. It is important to review and tweak the blueprint as needed to ensure the agent functions correctly.
After reviewing the blueprint, configure any specific parameters if needed:
Memory Settings: Decide whether the agent needs to store and retrieve data from past interactions or if it should operate statelessly.
Function Calls: Specify any external APIs or functions the agent needs to interact with to perform its tasks.
LLM and Tools Selection: If not already defined during the chat, select specific LLMs, tools, or data sources that the agent requires.
The platform offers a variety of built-in tools that can extend the agent’s capabilities. Choose the appropriate tools that match the agent’s goals:
Web Search: For retrieving information from the internet.
Data Scraping: For collecting data from websites.
Code Execution: For running scripts and performing calculations.
Image Generation: For creating visual content.
These tools are automatically integrated into the agent’s workflow, ensuring seamless operation.
Before deploying the agent, use the preview interface to interact with it in real time. Issue commands, ask questions, and evaluate its responses. This testing phase is crucial for refining the agent’s performance and ensuring it meets your expectations.
Once you’re satisfied with the agent’s configuration and performance, deploy it to the platform. The backend will handle the orchestration of computational resources, leveraging the distributed GPU network to ensure efficient execution of tasks.
After deployment, monitor the agent’s performance through the dashboard. The platform provides insights into resource utilization, task completion times, and overall efficiency. Use this data to make adjustments and optimizations as needed.
The platform’s backend automatically handles the orchestration of tools and memory for each agent. It selects the best combination of resources based on the defined goals and the available computational power from the distributed network. This ensures that each agent operates at optimal efficiency.
By leveraging the distributed GPU power from peers, the platform ensures that your agents have access to the necessary computational resources without the need for dedicated hardware. This decentralized approach not only reduces costs but also increases scalability and reliability.
