How to build agent-friendly products

AI agents aren’t just tools you build—they’re becoming users of your product.

If your SaaS app exposes an API, UI, or documentation, then agents are already trying to interact with it. Some are customer-built, others come from automation platforms or third-party agent frameworks.

The question is: Can an LLM-powered agent successfully use your product?

If the answer is no—or “only with hacks”—you’re leaving value (and customers) on the table.

Let’s see how you can fix that.

How agents use products

Agents don’t “explore” your app like a human would. They:

• Parse your API docs and try to follow them exactly
• Rely on tool/function schemas to call actions correctly
• Scan structured data for signal (not layout or styling)
• Follow UI element names and patterns to understand interface behavior
• Expect consistent, predictable responses—they don’t guess or infer

Agents are literal, procedural, and fragile. They don’t get sarcasm, handle inconsistency well, or intuit missing information.

This means we need to design our systems like we’re onboarding a very smart—but very robotic—junior engineer.

Making APIs agent-friendly

Agents interact with APIs using structured function calls. If your API is vague, inconsistent, or under-documented, they’ll fail (or worse—do the wrong thing).

1. Crystal clear documentation

Agents read documentation literally. They can't guess what you meant.

Your docs must be:

• Explicit about required/optional parameters
• Structured (use tables, lists, schemas)
• Formatted consistently
• Up-to-date (LLMs don’t handle ambiguity well)

	
// Good
POST /api/orders
Creates a new order. Returns order ID on success.

Required fields:
- customer_id (string): Existing customer ID
- items (array): List of product objects with id and quantity
- shipping_address (object): Full address with street, city, state, zip

Returns:
- 201: Order created successfully, returns {"order_id": "123", "status": "pending"}
- 400: Invalid input, returns {"error": "description"}
- 404: Customer not found


// Bad
POST /api/orders
Creates a new order (see examples below)
	

2. Useful, predictable error handling

Don't just return a 500. Tell the agent:

• What went wrong
• What it should do next
• Whether it should retry

	
{
  "error": "invalid_customer_id",
  "message": "Customer ID 'abc123' not found",
  "suggestion": "Check if customer exists or create a new customer first",
  "retry_after": null
}
	

3. Structured, predictable responses

Agents expect consistent response schemas. Agents hate surprises.

Always use:

• Fixed field names
• Explicit error codes
• Wrapped responses (e.g., { success: true, data: {...} })

	
// Good - consistent structure
{
  "success": true,
  "data": {
    "order_id": "123"
  },
  "metadata": {...}
}

// Bad - sometimes it's an array, sometimes an object
[{...}, {...}] // or {"results": [...]} // or just {...}
	

Making UIs agent-friendly

Even if you have APIs, some agents will still need to interact with your UI, using headless browsers or vision-based tools like GPT-4V.

1. Stable selectors

If your UI is dynamic, agents need reliable anchors.

<!-- Good - agents can reliably find this -->
<button id="submit-order" class="primary-action">Place Order</button>

<!-- Bad - generated classes change -->
<button class="btn-xyz123-temp">Place Order</button>

Use clear IDs and classes for key buttons, inputs, and forms. Avoid auto-generated junk.

2. Clear visual hierarchy

Vision agents need strong cues to understand context.

• Consistent layout and grouping
• Obvious call-to-actions
• Semantic HTML (use label, section, button, etc.)
• Avoid "mystery meat" UI (icons with no labels)

3. Visible loading and feedback

Agents need to know when to wait or retry. Spinners with no explanation are useless.

<!-- Show clear loading states -->
<button disabled>Processing... Please wait</button>‍

<!-- Not just a spinner with no context -->
<div class="spinner"></div>

Use ARIA labels or status messages where possible.

Make authentication agent-friendly

If you want agents to use your product safely and reliably, you need to support authentication flows they can handle.

Unlike humans, agents can’t click "Login with Google" or fill out multi-step forms. They need programmatic, documented, and predictable auth methods.

Support API Keys (for service agents)

Many agents are built for backend automation or internal tooling. For these, API keys are ideal:

• Simple to generate
• Easy to scope
• Work without UI

Best practices:

• Let users generate multiple keys with custom scopes
• Make it easy to rotate/revoke keys
• Return clear error messages for expired or invalid keys
• Track usage by key (rate limits, abuse detection)

Tip: Support per-agent keys so users can monitor agent behavior independently from human usage.

Support OAuth 2.0 (for agents acting as users)

If agents need to perform actions on behalf of users, your platform should support OAuth 2.0—ideally with agent-friendly flows.

What to support:

• Authorization code + refresh tokens (standard flow)
• Device code flow (for agents without a browser)
• Service accounts (for enterprise use cases)

Make sure your OAuth flow:

• Clearly defines scopes (e.g., read:orders, write:billing)
• Returns structured token responses (access_token, expires_in, scope, etc.)
• Provides clear error handling for invalid or expired tokens
• Includes refresh token support for long-lived sessions

Agent-friendly auth tips

• Provide sample cURL requests and code examples
• Document token lifetimes and refresh behavior
• Allow testing with real agents in sandbox environments
• Avoid login forms that require JavaScript, captchas, or UI tricks
• Don’t assume a browser is available

Rate limiting and usage behavior

Agents behave differently than humans. They can:

• Hit your APIs very quickly
• Retry multiple times
• Chain requests across tools
• Operate 24/7

You need to plan accordingly.

1. Smart rate limiting

• Let agents make several quick requests, then slow down.
• Use different limits for different types of agents.
• Use sliding windows or leaky buckets—not hard blocks
• Increase delays progressively, don't just block.
• Return helpful error messages on limit breaches

	
// Rate limit response
{
  "error": "rate_limited",
  "retry_after": 60,
  "limit": 100,
  "remaining": 0,
  "reset_time": "2025-06-25T10:30:00Z"
}
	

2. Usage monitoring

Track agent patterns:

• Request frequency
• Error rates
• Tool usage
• Cost spikes

Build dashboards to spot runaway agents or failing integrations.

3. Agent-friendly pricing

Design pricing models that agents can handle:

• Predictable costs (e.g., per call, per action)
• Usage caps (to prevent surprise bills)
• Volume discounts
• Line-item billing—show what each agent did and how much it cost

TL;DR: How to be agent-friendly

Testing your product for agent-friendliness

LLM agents and other machine users are already trying to use your product—through your APIs, your docs, your UI. The question is: are you testing your product like they do?

Because agents don’t behave like humans. They don’t “figure it out.” They follow instructions to the letter—and fall apart when things are unclear, inconsistent, or weird.

Here’s how to test your product as if a language model is the one using it—because in many cases, it is.

API usability testing

• Can a model understand how to use your API from the docs alone?
• Are required parameters, formats, and outputs clearly defined?
• Are error messages actionable (e.g., not just “400 Bad Request”)?
• Does the API return predictable, consistent JSON every time?

Tip: Test your docs with GPT-4 (“You are an agent. Here's the API documentation. Walk me through how to create an order.”)

Structured error response testing

Agents rely on structured, meaningful errors to recover or retry.

• Does every error include error_type, message, and suggestion?
• Do rate limit errors include retry_after?
• Are edge-case responses consistent across endpoints?

Avoid silent failures. Help agents help themselves.

Documentation robustness testing

Your documentation is now your product’s UI—for agents. Test it accordingly.

What to test:

• Can a language model read your docs and figure out how to use your API/tools?
• Do all examples actually work?
• Are required parameters clearly marked and explained?
• Are error codes and edge cases well documented?
• Is your OpenAPI/JSON schema consistent with your written docs?

Tip: Paste your docs into GPT-4 and prompt it like an agent. “How would you call this endpoint?” is a great starting point.

UI vision testing

If you expect agents to use your UI (e.g., through GPT-4V or RPA tools):

• Do key actions have stable ids or aria-labels?
• Are loading states visible and labeled?
• Are call-to-actions semantically tagged (<button>, <label>, etc.)?
• Can an agent find the right button, field, or link reliably?

Real-agent integration testing

Put a real agent (e.g., LangChain or CrewAI) in front of your product and try:

• Creating and reading records
• Following step-by-step docs
• Triggering known error conditions
• Interacting with both API and UI layers

Watch where it fails—those are your fix opportunities.

Checklist for agent-friendly products

• Documentation
  
  • API docs are literal and explicit, no assumptions required
  • Each endpoint lists required/optional parameters with types and formats
  • All example responses are complete, accurate, and copy-pastable
  • Docs use consistent terminology and field names
  • Error messages and response codes are clearly documented
  • Docs are versioned and easy to update (LLMs cache old info)
• API design
  
  • Endpoints follow consistent structure and naming (verbs, nouns, casing)
  • Response format is uniform (e.g., { success, data, metadata })
  • All responses are valid JSON with no ambiguity
  • API supports graceful error messages with clear error and suggestion fields
  • Endpoints return predictable error codes (400, 404, 429, etc.)
  • All tools or functions have clear descriptions (for agent tool calling)
• Error handling
  
  • All error responses explain what went wrong and what to do next
  • Errors include retry guidance (retry_after, rate limits, etc.)
  • No "silent failures" or inconsistent formats across endpoints
  • Rate limiting errors return helpful payloads (not just 429 Too Many Requests)
• UI (for agents using frontends)
  
  • Key elements have stable IDs or class names
  • Buttons, links, and inputs use semantic HTML (<button>, <label>, etc.)
  • Important actions are clearly labeled (no icons-only UIs)
  • Loading states are visible and descriptive (not just spinners)
  • No critical functionality hidden behind JavaScript-only logic
  • Error messages are displayed in the DOM, not just in console logs
• Authentication
  
  • Support API keys for programmatic access (with scopes & rotation)
  • Support OAuth 2.0 for agents acting on behalf of users and agent-friendly flows (e.g. device code)
  • Provide clear, structured token responses (access_token, expires_in, etc.)
  • Allow token refresh without manual re-authentication
  • Document all auth flows with agent-usable examples (cURL, JSON, etc.)
  • Offer sandbox/test credentials for agents to simulate workflows
  • Return helpful auth errors (invalid_token, expired_token, missing_scope)
  • Avoid login forms or CAPTCHAs in required agent flows
• Observability & monitoring
  
  • All requests are logged with agent/user attribution
  • Tool usage is tracked per agent or endpoint
  • Errors, retries, and failures are monitored and alertable
  • You can rate-limit or block abusive agents in real time
  • Usage analytics can distinguish between humans and agents
• Pricing & controls
  
  • Clear, predictable cost per call or operation
  • Support for volume discounts or usage tiers
  • Customers can set spending limits or usage caps
  • Billing dashboards show detailed per-agent usage
• Agent testing
  
  • You have test agents that simulate real behavior
  • Agents are tested on end-to-end workflows
  • API docs are run through LLMs to check for ambiguities
  • Vision-based agents are tested on the UI (e.g. GPT-4V, Selenium bots)
  • You monitor task completion rates and failure points
• Safety & fail-safes
  
  • Dangerous tools/actions require human review or confirmation
  • All actions are logged, rate-limited, and reversible
  • You can trigger an emergency stop for any agent
  • Escalation flows are in place for agent confusion or error loops
  • System is resilient to malformed inputs or agent retries

Final thoughts

AI agents are no longer hypothetical users—they're already navigating your APIs, reading your docs, and clicking through your UI. Whether they succeed or fail depends on how well your product speaks their language: literal, structured, and predictable.

The good news? Making your product agent-friendly doesn’t require a total overhaul. It just means applying the same design principles you use for great developer experiences—clear contracts, consistent behavior, and great documentation—with a little extra discipline for the things agents struggle with.

The next generation of users might not have keyboards or eyes—but they’ll still need to understand your system. Build for them now, and you’ll be ahead of the curve.

We’ll all be better off when your product is just as usable for an AI agent as it is for a human.