How to Build an Agent-First Product: Lessons from Stripe, Google, and Anthropic
Discover the design principles behind agent-first products, from payment rails to discovery APIs, and how to make your app callable by AI agents.
The Design Shift Nobody Warned You About
When you build software today, your primary user is no longer just a human sitting at a keyboard. Increasingly, it’s an AI agent calling your API, reading your documentation, and executing tasks on behalf of a person who may never directly touch your interface.
This is what “agent-first” product design means — building with the assumption that AI agents are first-class users of your product, not an afterthought. The companies doing this best right now — Stripe, Google, and Anthropic — aren’t just adding AI features. They’re rethinking what their products need to expose, how they communicate capabilities, and what it means for software to be interoperable with autonomous systems.
This post breaks down what each of them is doing, why it works, and what you can take from their approaches to build products that agents can actually use.
Why Agent-First Design Is Different from API Design
Good API design and agent-first design share DNA, but they’re not the same thing.
A well-designed REST API is built for developers who read documentation, understand context, and write code to interact with it. An agent-first product is built for systems that need to discover what’s possible, understand intent, execute actions, and recover from errors — all without a human in the loop.
That changes several things:
- Discovery: Agents need to find out what your product can do at runtime, not just from static docs
- Clarity: Every action and parameter needs unambiguous descriptions an LLM can reason about
- Atomicity: Operations should be scoped to single, clear actions — not multi-step wizards
- Error handling: Agents need structured, meaningful errors they can act on, not just HTTP codes
- Authentication: Agents operate across sessions and don’t manage cookies or OAuth flows like humans do
Other agents ship a demo. Remy ships an app.
Real backend. Real database. Real auth. Real plumbing. Remy has it all.
The companies leading here have figured out that serving agents well often means serving human developers better too. The two goals reinforce each other.
Stripe’s Playbook: Payment Rails Built for Agents
Stripe has spent over a decade making payment infrastructure developer-friendly. That same philosophy translated naturally to agent-first design when the time came.
The Stripe Agent Toolkit
In early 2024, Stripe released the Stripe Agent Toolkit — a set of official integrations that lets AI agents interact with Stripe’s payment APIs through popular frameworks like LangChain, CrewAI, Vercel AI SDK, and Anthropic’s tool-use API.
The toolkit exposes a curated set of Stripe operations as typed tools: creating payment links, retrieving customer data, managing subscriptions, issuing refunds. Each tool has a clear name, a description the LLM uses to decide when to call it, and a well-typed parameter schema.
This is more important than it sounds. Most APIs expose hundreds of endpoints. If you hand an agent your entire OpenAPI spec, it doesn’t know which endpoints to use, what order to call them in, or what’s safe to trigger autonomously. The toolkit solves that by curating the right surface area — only the operations that make sense for an agent to run.
What Stripe Got Right
A few things stand out about Stripe’s approach:
Idempotency by default. Stripe has always required idempotency keys on mutations. For agents that might retry a failed request, this prevents double-charges. It’s a safety property that was baked in long before agents existed, but it matters more now.
Explicit capability scope. The toolkit lets developers control exactly which Stripe operations an agent can use. You don’t give an agent “access to Stripe” — you give it access to a specific list of actions. That scoping keeps agents from doing things they shouldn’t.
Human-readable error messages. When something fails, Stripe returns errors that describe what went wrong and why. An agent can read “No such customer: ‘cus_abc123’” and reason about it. Contrast that with a generic 400 error.
Separation of read and write operations. The toolkit makes it easy to build agents that can query data without being able to mutate it. That’s a useful trust boundary when you’re letting an agent access financial data.
The Broader Lesson
Stripe’s approach shows that agent-first design isn’t about building something new on top of your existing product. It’s about surfacing your existing capabilities in a form that AI systems can use reliably. The underlying API didn’t change — the interface layer did.
Google’s Approach: Make Everything Discoverable
Google’s problem is the inverse of most companies. They don’t need agents to use their product — they need to build agents that work across the entire internet. That gives them a different perspective on agent-first design.
The Agent2Agent Protocol
In April 2025, Google introduced the Agent2Agent (A2A) protocol, an open standard for interoperability between AI agents from different vendors and platforms. The core idea: agents should be able to discover what other agents can do and delegate tasks to them, even when they’re built on different systems.
Remy is new. The platform isn't.
Remy is the latest expression of years of platform work. Not a hastily wrapped LLM.
A2A works alongside Anthropic’s Model Context Protocol (MCP) — MCP handles how agents connect to tools and data sources, while A2A handles how agents communicate with each other. Together, they sketch out a plausible infrastructure for multi-agent systems at scale.
Google’s Discovery API Philosophy
Google has consistently bet on making services machine-discoverable. Their Discovery API format lets tools advertise their capabilities in a structured, queryable way. This matters for agents because the alternative — hard-coding what every service can do — doesn’t scale.
When an agent encounters a new service, it needs to answer: what can this thing do? What parameters does it accept? What does it return? Static documentation written for humans doesn’t answer those questions efficiently. Structured capability manifests do.
The principle here extends beyond Google. If you want your product to be used by agents, it needs to explain itself in a format agents can consume — not just humans.
Gemini’s Function Calling Architecture
On the model side, Google’s Gemini API has robust support for function calling with parallel execution — the ability to call multiple tools simultaneously when tasks can be done in parallel. This is important for building agents that are fast in production, not just correct.
Gemini’s approach to structured outputs (returning JSON that matches a specific schema) also makes it easier to build reliable agent pipelines. When you know exactly what shape the output will be, you can chain steps without fragile parsing logic.
Anthropic’s Model Context Protocol: A Standard Worth Understanding
If there’s one technical development in the agent space that every product builder should understand right now, it’s the Model Context Protocol (MCP).
What MCP Actually Is
MCP is an open standard Anthropic released in late 2024 that defines how AI models connect to external data sources and tools. Think of it as a universal connector between AI assistants and the services they need to access.
Before MCP, every AI integration was a custom build. You’d write a specific plugin for Claude, a different one for GPT, another for Gemini. MCP proposes a shared protocol so that one server implementation works with any compliant model.
An MCP server exposes:
- Resources — data the model can read (files, database records, API responses)
- Tools — actions the model can take (create, update, delete, send)
- Prompts — reusable prompt templates for common workflows
The model client handles the connection, authentication, and protocol details. The server handles the actual operations. Developers write the server once; it works anywhere MCP is supported.
Why This Matters for Product Builders
If you maintain a product with an API, building an MCP server for it is one of the highest-leverage things you can do right now. It means your product is instantly usable by any MCP-compatible AI assistant — Claude, and increasingly others.
The cost is relatively low (MCP servers can be simple Node or Python processes), and the benefit is substantial: your product becomes a first-class capability inside AI assistants your users are already using.
Anthropic has published a full spec and SDK. The community has built hundreds of MCP servers already, covering everything from GitHub to Postgres to Slack.
Tool Use and the Design of Good Tools
Anthropic’s work on Claude’s tool use has produced useful thinking on what makes an AI tool good or bad. A few principles they’ve documented:
Names should be unambiguous. get_customer_by_id is better than customer_lookup. The model needs to match a user’s intent to the right tool, and ambiguous names cause mistakes.
Descriptions should explain when to use the tool, not just what it does. “Use this when the user wants to view a customer’s billing history” is more useful than “Returns customer billing records.”
Parameters should have clear types and constraints. If a date field expects ISO 8601 format, say so in the description. The model will get it right more often.
Keep tools atomic. A tool that does one thing is easier to use correctly than a tool that does multiple things depending on which parameters you pass.
Five Design Principles for Agent-First Products
Across these three companies, some patterns show up consistently. Here are the principles worth adopting.
1. Expose a Curated Tool Surface
Don’t expose everything. Pick the 10–20 operations that make sense for an agent to perform autonomously and build clean tool definitions around them. More isn’t better — it creates ambiguity and increases the chance of mistakes.
Think about: what tasks would a user want to delegate to an agent? Start there.
2. Write Descriptions for LLMs, Not Humans
Every tool, parameter, and resource in your agent interface needs a description. Those descriptions are what the model uses to decide when and how to use your product. If they’re vague or missing, the model will guess — and guessing means errors.
Test your descriptions by asking a model to use your tool from a plain-language request. If it picks the wrong tool or passes wrong parameters, your description needs work.
3. Make Errors Informative and Actionable
Agents retry. When something fails, the agent needs enough context to either fix the request or escalate to the user. Structured errors with error codes, human-readable messages, and hints about what to do next make your product far more reliable in agent workflows.
A bare 422 Unprocessable Entity with no body helps nobody — human or otherwise.
4. Build for Minimal Permissions
Design your agent integration so that each agent or workflow can be granted the minimum permissions it needs. This isn’t just a security practice — it’s a product feature. Developers building with your product want to give their agents narrow scope, not a master key.
Permission scoping also makes it easier to audit what agents did and why.
5. Support Structured Outputs Throughout
Wherever your product returns data, make that data structured and schema-validated. Agents that receive well-shaped JSON can pass it directly to the next step in a workflow. Agents that receive unstructured text have to parse it, which introduces errors.
This applies to errors too — structured error objects are easier to handle than error messages embedded in HTML pages.
Making Your App Callable by AI Agents: A Practical Checklist
If you want to move from “has an API” to “agent-ready,” here’s what to actually do.
Not a coding agent. A product manager.
Remy doesn't type the next file. Remy runs the project — manages the agents, coordinates the layers, ships the app.
Define your agent surface area. List the 10–20 operations that make the most sense for autonomous use. Skip anything that requires complex multi-step human confirmation.
Write an OpenAPI spec or MCP server. Both serve similar purposes — they give agents a structured, machine-readable description of what you can do. OpenAPI is more established; MCP is gaining ground fast for AI-native integrations.
Add tool-quality descriptions. Every endpoint, parameter, and return value needs a clear description written with LLM reasoning in mind. This is the most underinvested area in most API docs.
Test with real agents. Run Claude or GPT against your spec and give it realistic tasks. Watch where it fails — wrong tool selection, bad parameter values, mishandled errors. Fix those.
Implement idempotency on mutations. Agents will retry. Make it safe for them to do so.
Create a scoped authentication path. Let developers issue API keys or tokens scoped to specific operations. Don’t make “give the agent full access” the only option.
Handle long-running operations. Many agentic tasks take time. Support webhooks or polling endpoints so agents can check status without blocking.
How MindStudio Fits into an Agent-First Architecture
Building the server side of agent-first design is only half the picture. You also need a way to build, deploy, and connect agents that can consume these capabilities.
This is where MindStudio is worth knowing about. It’s a no-code platform for building AI agents and automated workflows — and it’s specifically designed for the kind of multi-step, tool-using agent behavior described in this post.
The most relevant capability here is MindStudio’s support for agentic MCP servers — you can expose MindStudio agents as MCP servers that other AI systems can call. If you’ve built a workflow in MindStudio (say, a lead enrichment process or a document review pipeline), it becomes a callable capability for Claude, LangChain, CrewAI, or any other MCP-compatible system.
MindStudio also ships with 1,000+ pre-built integrations — Salesforce, HubSpot, Google Workspace, Slack, and more — so you can build agents that already have access to the tools your workflows need, without writing connection code.
For teams that want to build AI-powered workflows without managing infrastructure, it handles the reliability layer: retries, rate limiting, auth, and logging. Your agents focus on reasoning and action, not plumbing.
You can try MindStudio free at mindstudio.ai.
Frequently Asked Questions
What does “agent-first” mean in product design?
Agent-first design means treating AI agents as primary users of your product, not an afterthought. It involves structuring your APIs, tool definitions, error handling, and authentication specifically for how agents discover and use capabilities — rather than just optimizing for human developers or end users.
What is the Model Context Protocol (MCP) and why does it matter?
MCP is an open standard published by Anthropic that defines how AI models connect to external tools, data sources, and services. It creates a shared protocol so that one server implementation works with any compliant AI assistant. For product builders, it’s one of the most practical ways to make your product available inside AI workflows today.
How is agent-first product design different from building a REST API?
A REST API is built for developers who write code against it. An agent-first product is designed for AI systems that need to discover capabilities at runtime, understand intent from descriptions, and operate without human guidance. This means tool descriptions, scoped permissions, structured errors, and idempotency all matter more than they typically do in traditional API design.
What can I learn from Stripe’s approach to AI agents?
Stripe’s Agent Toolkit shows the value of curating a specific tool surface area for agents, enforcing idempotency to make retries safe, providing rich error messages, and giving developers control over exactly which operations an agent can perform. The underlying API didn’t change — the agent-facing layer did.
How do I make my existing product work with AI agents?
Start by writing an MCP server or OpenAPI spec with tool-quality descriptions for your key operations. Test it with real AI agents running realistic tasks. Add idempotency to mutations, structure your error responses, and create a scoped authentication path. You don’t need to rebuild your product — you need to expose it properly.
What is the Agent2Agent (A2A) protocol from Google?
A2A is an open standard Google introduced in 2025 for interoperability between AI agents from different platforms. It defines how agents discover each other’s capabilities and delegate tasks across systems. It’s designed to work alongside MCP — MCP handles agent-to-tool connections, while A2A handles agent-to-agent communication.
Key Takeaways
- Agent-first design is about building for AI agents as first-class users — not just adding an AI layer on top of existing products
- Stripe, Google, and Anthropic all prioritize curated tool surfaces, structured outputs, and clear capability descriptions — not just open-ended APIs
- MCP is the closest thing to a standard for connecting AI systems to tools and services right now; building an MCP server for your product is high-leverage work
- Good agent tool design means writing descriptions for LLM reasoning, not for human readers — the description is what determines correct tool selection
- Scoped permissions, structured errors, and idempotency aren’t optional in agent-first products — they’re what make agents reliable in production
- Building multi-agent workflows that connect to agent-first products is increasingly accessible with platforms like MindStudio — start building free
