Overview

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Agent Overview

• Data processing APIs
• AI chatbots
• Web-based AI solutions
• AI-powered applications
• AI tools for specific tasks
• AI assistants for specific domains
• Analytics and reporting tools
• Data Analysis & Dashboards for data visualization

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Key Points

• Creators should create understand Agent functionality in terms of APIs, webpages, Chatbots served by the agent.

• Agents are deployed in Docker containers, which provide a secure and isolated environment for running the agent’s code.

• Each agent can have its own set of environment variables, which can be used to store sensitive information such as API keys, database credentials, and other configuration settings.
• Environment variables (Secrets) must be set up in the Creator’s Secret manager to be accessed for the agent’s Docker container.

• Agents can be configured to require user authorization before they can be accessed e.g. Premium Agents.
• This can be done by implementing an authorization mechanism in the agent’s code i.e. API call to check User access to the Agent.
• The agent can then check the user’s authorization status before allowing access to its functionality.

• Agents can be configured to require credit verification before they can be accessed e.g. All Agents.
• This can be done by implementing a credit verification mechanism in the agent’s code i.e. API call to check User credits to access the Agent.
• The agent can then check the user’s credit status before allowing access to its functionality.

• Agents can be integrated with any type of AI models, LLM, SLM etc to enhance their capabilities and provide more advanced functionality.
• This can be done by using the LLM API-KEYs to send requests and receive responses from the LLM.
• The agent can then process the LLM’s response and use it to provide more accurate and relevant results to the user.

• Integrations stand for the ability to connect and interact with other services, APIs, and platforms e.g. Slack, Google, Zoho etc.
• Agents can be enhance their functionality and provide more advanced features by using mechanism of API-Keys & Integration SDKs.
• This can be done by using APIs to send requests and receive responses from other services.
• The agent can then process the response and use it to provide more accurate and relevant results to the user.

• Agents should be designed to handle a large number of requests and provide fast response times.
• This can be achieved by optimizing the agent’s code, using caching mechanisms, and implementing load balancing techniques.

• Agents should be designed with security in mind, including protecting sensitive information, preventing unauthorized access, and ensuring data integrity.
• This can be achieved by implementing secure coding practices, using encryption, and following best practices for securing APIs and web applications.

• Agents should be thoroughly tested before deployment to ensure they function as intended and meet the requirements of the users.
• This can be achieved by implementing unit tests, integration tests, and end-to-end tests to verify the agent’s functionality and performance.

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Expectations

• Creating agents that are user-friendly and easy to understand
• Ensuring agents are capable of handling user requests efficiently and effectively
• Providing clear and concise documentation for users to understand how to interact with the agents
• Ensuring agents are secure and do not expose sensitive information or vulnerabilities
• Following best practices for coding and deployment to ensure reliability and maintainability
• Testing agents thoroughly before deployment to ensure they function as intended
• Monitoring agent performance and user feedback to make improvements over time
• Providing support and assistance to users as needed
• Keeping agents up to date with the latest features and improvements from OptimalAgents.ai
• Ensuring agents are compliant with any relevant regulations or guidelines