Build, run, and scale AI agents like APIs and microservices, making them observable, auditable, and identity-aware from day one.
Automations like a personal customer support chatbot, triggered by emails, that interacts with APIs to retrieve user data and send personalized messages.
It helps you create scalable and secure AI agents, reducing the risk of errors and improving accountability, while accelerating your development process.
"A founder can use Agentfield to build a chatbot that integrates with their Stripe customers' information, automatically sending a welcome email and tracking customer interactions. To set it up, she creates an Agentfield API, configures the Stripe webhook integration, and writes a custom logic to interact with the chatbot. With Agentfield, she can monitor and control the chatbot's activities, ensuring a seamless customer experience."
Beginners can start with this when they want to explore creating simple AI agents without extensive expertise in machine learning or large amounts of data.
Senior engineers and professionals will reach for this when they need to build scalable and secure AI agents that require fine-grained control and monitoring.
This skill allows for the creation of AI agents that are already observable, auditable, and identity-aware, which might differ from other solutions that require post-deployment setup for these features.
Agentfield allows startup founders to build, run, and scale AI agents as observable, auditable, and identity-aware API and microservices from day one.
go get github.com/Agent-Field/agentfield/cmd/agentfieldRun 'agentfield run --config agentfield.yaml --workflow workflow.yaml' to start an agentfield server with a custom configuration and workflow
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