--- title: "AI Week: What Autonomous Agents Actually Need from Your APIs" description: "AI agents need fundamentally different APIs than human developers. Emmanuel Paraskakis breaks down the authentication, billing, and design challenges most companies face when preparing their APIs for autonomous agent traffic, plus his top three priorities for getting ready." canonicalUrl: "https://zuplo.com/blog/2025/10/03/what-autonomous-agents-actually-need-from-your-apis" pageType: "blog" date: "2025-10-03" authors: "martyn" tags: "AI" image: "https://zuplo.com/og?text=What%20Autonomous%20Agents%20Actually%20Need%20from%20Your%20APIs" --- AI agents aren't just another type of API consumer. They're fundamentally different from human developers, and most API infrastructure isn't ready for them. As part of AI Week at Zuplo, celebrating the [launch of our AI Gateway](/blog/zuplo-ai-gateway), I sat down with [Emmanuel Paraskakis](https://www.linkedin.com/in/emmanuelparaskakis/) to discuss what it really takes to make APIs agent-ready. ## The Uncomfortable Truth About API Readiness One of the most striking insights from our conversation was how unprepared most companies are for agentic traffic, even organizations that consider themselves API-first. Emmanuel teaches courses on this topic, primarily to product managers, and consistently finds that people arrive thinking their infrastructure is solid, only to discover they need to go back to basics. The problem isn't just about adding a new feature or endpoint. It's about rethinking fundamental assumptions around API design, documentation, authentication, and business models that were built for human developers, not autonomous agents. ## Why Agents Are Different When a human developer integrates with your API, they can navigate complexity. They can read between the lines of documentation, figure out multi-step workflows, and wait days or weeks for contract negotiations and provisioning. Agents can't, and they won't. Agents need APIs that are instantly discoverable and provisionable. They need simple, focused endpoints that accomplish specific use cases without requiring complex payload assembly. They need clear, accurate machine-readable documentation because they can't make intuitive leaps the way humans can. And they need all of this to happen without friction, or they'll simply move on to another data source, or erroneously hammer an API with their own approximation of what they believe they should do. As Emmanuel points out, agents are persistent and helpful, but they don't have long attention spans. If your onboarding process involves DocuSign contracts and sales calls, you're not in the game. ## The MCP Factor Model Context Protocol (MCP) is rapidly becoming the standard way for agents to discover and interact with APIs and other data sources. While MCP is still evolving (authentication and authorization are still being refined in the spec), it's clear that having an MCP server is quickly becoming a necessary addition. The good news is that if you have solid OpenAPI documentation with high fidelity to your actual API implementation, building an MCP server becomes much more straightforward. The bad news is that many organizations discover their API documentation is incomplete, outdated, or inconsistent with what's actually deployed. ## Authentication, Billing, and the Autonomy Challenge Some of the thorniest problems in the agentic API world revolve around authentication and payment. How does an autonomous agent authenticate with your API when there's no human to complete an OAuth flow? How does it pay for usage when it doesn't have a credit card? We're seeing emerging solutions like [Google's recently announced AP2 payment protocol](https://cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol), but the ecosystem is still maturing. In the meantime, organizations need to think hard about how to reduce friction in these areas while maintaining appropriate security and control. The shift to usage-based billing is another challenge, particularly for organizations whose entire financial infrastructure is built around per-seat pricing. This isn't just a technical problem. It touches finance, legal, and sales processes that may take significant time to overhaul. ## The Top Three Priorities If you're looking to be in a more prepared position, and you have limited time, Emmanuel recommends: 1. **Publish your own MCP server.** Get something out there that you control, start gathering feedback, and begin learning how agents interact with your APIs in practice. 2. **Simplify relentlessly.** Look for opportunities to reduce steps, aggregate operations, and create use-case-specific endpoints that make it dead simple for agents to accomplish tasks. 3. **Fix your API development process.** Embrace spec-first design, ensure what you build matches what you document, and implement proper testing and governance. The boring fundamentals matter more than ever. ## Looking Ahead Emmanuel predicts that in 18 months, we'll see significant progress in the MCP specification, better tooling and practices around agent-ready APIs, and hopefully solutions to the prompt injection challenges that could otherwise slow adoption. But he's also clear that organizations need to act now. Agentic traffic isn't a future possibility. It's already here. Your customers are experimenting with agents, and they're asking about MCP servers and roadmaps. The question isn't whether to prepare, but whether you'll be ready in time. ## More from AI Week This article is part of Zuplo's AI Week. A week dedicated to AI, LLMs and, of course, APIs centered around the release of our [AI Gateway](https://zuplo.com/ai-gateway). You can find the other articles and videos from this week below: - Day 1: [AI Gateway Overview](/blog/zuplo-ai-gateway) with Zuplo CEO, Josh Twist - Day 2: [Is Spec-Driven AI Development the Future?](/blog/spec-driven-ai-development) with Guy Podjarny, CEO & Founder of Tessl - Day 2: [Using AI Gateway with LangChain & OpenAI](/blog/ai-gateway-with-langchain) with John McBride, Staff Software Engineer at Zuplo - Day 3: [Your AI Models Aren't Learning From Production Data](/blog/comet-ml-opik) with Gideon Mendels, CEO & Co-Founder of Comet ML - Day 3: [Using Claude Code with Zuplo's AI Gateway](/blog/ai-gateway-with-claude-code) with Martyn Davies, Developer Advocate at Zuplo - Day 4: [What Autonomous Agents Actually Need from Your APIs](/blog/what-autonomous-agents-actually-need-from-your-apis) with Emmanuel Paraskakis, CEO of Level250 - Day 4: [Using AI Gateway with goose AI agent](/blog/ai-gateway-with-goose) with Martyn Davies, Developer Advocate at Zuplo