Building APIs for Developers and AI Agents
Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
Podcast Summary
In this insightful episode, Stainless founder Alex explores the evolution of APIs from simple connective tissue to the neural pathways of internet infrastructure. He shares his journey from building world-class developer platforms at Stripe to democratizing SDK generation for every API company. The conversation dives deep into the emerging Model Context Protocol (MCP) (06:00), where APIs become interfaces for AI agents rather than just human developers. Alex reveals how context window limitations and tool overwhelm create new design challenges (11:28), requiring innovative solutions like dynamic tool exposure and JQ filtering to keep AI interactions focused and efficient. The discussion culminates with predictions about the future of API development, where high-level business logic takes priority while automated systems handle the intricate details of robust, type-safe developer experiences.
Speakers
Alex (Stainless Founder)
Former Stripe API platform engineer who built their renowned API docs redesign and SDK code generation system. Founded Stainless to bring enterprise-grade developer platforms to every API company, working with cutting-edge clients like OpenAI, Anthropic, and Cloudflare.
Jennifer Lee (Host)
Partner at Andreessen Horowitz (a16z), focusing on developer tools and infrastructure investments. Leads conversations on the future of APIs, AI agents, and developer experience platforms.
Key Takeaways
Treat SDKs as Your Primary Developer Interface
To developers using your API daily, the SDK IS the API. Focus on making SDKs robust, polished, and idiomatic rather than just generating basic wrappers. (02:08) Your users expect well-typed libraries in their language as a baseline developer experience requirement.
Master the Art of Context Window Economy
When exposing APIs to LLMs through MCP, naive approaches consume entire context windows. Implement dynamic tooling with only 3 core functions: list endpoints, describe single endpoint, execute endpoint. This scales O(1) instead of O(n) while preserving flexibility. (13:34)
Enable Surgical Data Extraction with JQ Filtering
Large API responses kill LLM interactions. Build JQ filter capabilities into your tools so agents can specify exactly which JSON properties they need, mimicking SQL's SELECT clause. LLMs excel at crafting these filters, keeping context usage minimal. (15:51)
Leverage Type Safety as Your Quality Gate
Coding agents prefer SDKs over raw API calls because type checking provides immediate feedback loops. Those "red squiggly lines" catch errors before production, enabling human reviewers to focus on business logic rather than syntax debugging. (18:55)
Design Declarative, DRY Code Architecture
As LLMs handle more coding tasks, prioritize clean, declarative backends over "spaghetti" REST implementations. Establish prescriptive API design standards that agents can follow consistently, letting humans focus on high-level business needs while automation handles infrastructure details. (25:34)
Statistics & Facts
- The Stripe team used a meta-approach where (02:08) "to the developers who use the Stripe API every day, the SDK is the API" - highlighting that developers primarily interact through SDKs rather than raw APIs.
- Context window limitations force a critical trade-off: describing every endpoint in the Stripe API with full documentation (12:15) "congratulations, you've just used up your entire context window" when exposing APIs to LLMs via MCP.
- Stainless reduced API tool complexity from O(n) to O(1) by implementing dynamic mode: (14:14) "instead of say, you know, 50 or 500 different tools, we only expose three" for listing, describing, and executing endpoints.
