Problem solved

Developers building AI agents often face significant overhead when integrating external services: they need to research, authenticate, and maintain multiple APIs, each with its own pricing and usage limits. This fragmentation complicates development and can delay time-to-market. _done solves this by aggregating a wide range of tools and APIs into a single, agent-focused platform with a pay-per-use model. This eliminates upfront integration effort, reduces costs by charging only for actual usage, and provides a frictionless experience, allowing developers to focus on agent logic rather than infrastructure.

Use cases

• Integrating payment processing into a conversational commerce agent
• Fetching real-time weather, news, or stock data for a personal assistant agent
• Scheduling meetings and sending calendar invites through an AI scheduling agent
• Automating multi-step workflows (e.g., order placement, email notifications) within an enterprise AI agent
• Performing sentiment analysis or language translation on user input in a customer support agent
• Accessing geolocation services for a delivery or logistics agent

Audience

• Developers
• ML Engineers
• Data Scientists

Pros

• Pay-per-use pricing reduces costs and eliminates upfront commitments
• Low friction setup with a unified API designed for AI agent integration
• Broad selection of tools and APIs tailored for common agent tasks
• Simplifies agent development by abstracting complex third-party integrations

Cons

• Limited documentation or community support may hinder initial adoption (inferred from lack of available details)
• Dependency on third-party APIs could introduce latency or reliability issues
• Service breadth may be insufficient for highly specialized agent tasks