Building APIs for Developers and AI Agents
a16z PodcastFull Title
Building APIs for Developers and AI Agents
Summary
The episode discusses the evolution of APIs as critical infrastructure for both human developers and AI agents, emphasizing the importance of robust developer platforms, SDKs, and documentation.
It highlights the challenges and new frontiers in designing interfaces for AI agents, particularly with the emergence of MCPs (Model Context Protocols), and how this impacts the future of API development and user experience.
Key Points
- APIs are fundamental to the internet's functionality, acting as the connective tissue for programs to interact and "think."
- Every company is becoming an API company, necessitating dedicated API platforms that include well-crafted SDKs, documentation, and versioning to allow developers to focus on core capabilities.
- Building high-quality SDKs is crucial, requiring them to be robust against internet errors, scalable, offer clear error messages, support telemetry, and be idiomatic to the target programming language and developer.
- The emergence of AI agents as users of APIs necessitates new interface designs, with MCPs reframing APIs as interfaces for large language models.
- Providing AI agents with effective API access presents challenges like context window limitations and efficient tool selection, requiring dynamic approaches to expose relevant tools and parameters.
- The quality of APIs and SDKs remains paramount, even with AI agents, as strongly typed and polished interfaces enhance accuracy, debuggability, and overall developer experience.
- MCPs can also serve as a powerful tool for AI agents to access API documentation, ensuring they use the correct SDK versions and understand the interface, preventing common integration errors.
- The future of API development will involve designing for both human developers and AI agents, leading to more declarative, dry, and type-safe code, and clearer standards for API design.
Conclusion
High-quality API platforms, including robust SDKs and clear documentation, are essential for both human developers and the emerging generation of AI agents.
The development of MCPs signifies a shift towards designing APIs as interfaces for AI, presenting new challenges and opportunities in how we build and interact with software.
As AI integration becomes more prevalent, the emphasis on well-designed, idiomatic, and type-safe interfaces will only increase, ensuring accuracy, efficiency, and confidence in software development.
Discussion Topics
- How are AI agents changing the fundamental requirements for API design and documentation?
- What are the biggest challenges in creating idiomatic and robust SDKs for both human developers and AI agents?
- In the future, how will the relationship between API developers, AI agents, and end-users evolve?
Key Terms
- API
- Application Programming Interface, a set of rules and protocols that allows different software applications to communicate with each other.
- SDK
- Software Development Kit, a collection of tools, libraries, documentation, and code samples that help developers create applications for a specific platform or technology.
- LLM
- Large Language Model, a type of artificial intelligence model trained on vast amounts of text data to understand and generate human-like text.
- MCP
- Model Context Protocol, a protocol for exposing APIs to large language models, enabling them to interact with external services.
- JSON Schema
- A vocabulary that allows you to annotate and validate JSON documents.
- JQ
- A lightweight and flexible command-line JSON processor.
Timeline
APIs are described as the dendrites of the internet, enabling interaction between programs.
The premise that every company is becoming an API company and requires a strong API platform with comprehensive tooling is introduced.
The growing complexity and challenges in API usage for developers, including SDK quality and documentation, are highlighted, along with the new frontier of AI agents.
The speaker's experience at Stripe, focusing on improving API documentation and the critical role of SDKs as the developer's primary interface to an API, is detailed.
The difficulty and maintenance burden of generating high-quality SDKs for various languages are discussed, leading to the founding of Stainless API to provide these solutions.
The discussion shifts to the approach of SDK generation, emphasizing the importance of robustness, polish, and idiomatic design for developer experience.
The introduction of MCPs as a new frontier for APIs, framing them as interfaces for LLMs, and the challenges of designing for AI agents are explored.
The "dendrites of the internet" metaphor is revisited, and MCPs are discussed as essential interfaces for the AI era.
The evolution from user interfaces to APIs as ways for computers to interact with the real world is explained, with MCPs being the next step for AI interaction.
The impact of LLMs as a new user persona on API design and tool development is considered.
MCP is defined as the interface between an API and an LLM, analogous to an SDK for a human programmer.
The practical implementation of MCPs and the challenge of managing context windows when exposing numerous API endpoints and parameters to LLMs are detailed.
Solutions for managing context window limitations, including static and dynamic exposure of API tools and parameters, are presented.
The use of JQ filters to manage large API responses and reduce context window usage for LLMs is discussed.
The misconception that API/SDK quality is less important with MCPs is addressed, arguing that high-quality interfaces are even more critical.
The importance of using up-to-date SDKs and accessing accurate documentation for AI coding agents to prevent integration errors and hallucinations is explained.
The benefits of strongly typed SDKs for LLM-assisted coding, including improved debuggability and accuracy, are highlighted.
The critical need for companies to provide easy and confident integration paths for APIs, especially for production use, is emphasized.
The future role of API developers in five to ten years, focusing on higher-level design and working alongside AI agents, is contemplated.
The trend towards more declarative, dry, and type-safe code, and the importance of clear API design standards for LLM adoption, are discussed.
The painful and difficult aspects of API design and development are contrasted with the ideal scenario of letting AI handle lower-level tasks.
Lessons learned from collaborations with cutting-edge companies like OpenAI, Anthropic, and Cloudflare regarding SDK design and API platforms are shared.
The fundamental importance of developers focusing on core capabilities and thoughtfully exposing interfaces, without worrying about low-level details, is reiterated.
Episode Details
- Podcast
- a16z Podcast
- Episode
- Building APIs for Developers and AI Agents
- Official Link
- https://a16z.com/podcasts/a16z-podcast/
- Published
- September 6, 2025