--- title: "How to Build Software for AI Agents: APIs, Auth, and Tooling" author: "Nate Laquis" author_role: "Founder & CEO" date: "2028-05-21" category: "How to Build" tags: - build software for AI agents - agent-friendly APIs - MCP server development - AI agent tooling - machine-readable APIs excerpt: "AI agents are becoming users of your software. If your product is not agent-friendly, you are losing a fast-growing customer segment. Here is how to build software that agents can actually use." reading_time: "16 min read" canonical_url: "https://kanopylabs.com/blog/how-to-build-software-for-ai-agents" --- # How to Build Software for AI Agents: APIs, Auth, and Tooling ## Why Your Software Needs to Work for AI Agents The user base for software is expanding beyond humans. AI agents now perform tasks that humans used to do manually: scheduling meetings, managing CRM pipelines, processing invoices, deploying code, and purchasing supplies. These agents interact with software through APIs, and most existing APIs were not designed for agent consumption. The difference between a human-usable API and an agent-usable API is significant. Humans read documentation, understand context, and adapt to ambiguity. Agents need structured, predictable, and self-describing interfaces. When an agent hits an error, it cannot "figure it out" the way a human developer can. It needs clear error codes, actionable error messages, and deterministic behavior. Companies that make their software agent-friendly are seeing tangible growth. Stripe's agent-optimized APIs have driven a new category of automated financial operations. Linear's clean API makes it a favorite for AI-powered engineering workflows. Products with poor agent ergonomics are being replaced by competitors that agents can work with more reliably. This is not a future trend. It is happening now. If you are building a B2B SaaS product, your next power user might be an AI agent, not a person sitting at a keyboard. ![Code on monitor showing API development for AI agent integration](https://images.unsplash.com/photo-1461749280684-dccba630e2f6?w=800&q=80) ## Designing Machine-Readable APIs Agent-friendly APIs follow specific design principles that go beyond standard REST or GraphQL best practices. ### Semantic Naming Agents interpret endpoint names to understand functionality. /api/v1/invoices/create is better than /api/v1/inv/c. Use full words, consistent patterns, and names that describe the action clearly. The endpoint name is the agent's first signal for whether an API call is appropriate for its task. ### Structured Error Responses Return errors with machine-parseable codes, human-readable messages, and suggested fixes. Instead of returning 400 Bad Request with a string message, return structured JSON with an error_code, a message explaining what went wrong, a suggestion for how to fix it, and a documentation_url pointing to the relevant docs. Agents use error responses to decide whether to retry, modify their approach, or escalate to a human. Vague errors cause infinite retry loops. Specific errors enable intelligent recovery. ### Comprehensive OpenAPI Specifications Publish an OpenAPI (Swagger) specification that describes every endpoint, parameter, response type, and error code. Include descriptions for every field, not just names. Agents use these descriptions to understand what data to provide and what to expect in return. A field called "status" with no description is useless to an agent. "status: The current fulfillment state of the order. One of: pending, processing, shipped, delivered, cancelled" is actionable. ### Idempotent Operations Agents retry failed requests. If your create endpoint is not idempotent, retries create duplicates. Use idempotency keys for all write operations. Accept a client-generated unique key with each request and return the same response if that key has been seen before. Stripe popularized this pattern, and it is essential for agent reliability. ### Pagination Done Right Cursor-based pagination is better than offset-based for agents. Cursors are stable across data changes and easier for agents to manage. Always include total_count, has_more, and next_cursor in paginated responses so agents can determine whether they need to fetch more data. ## Authentication and Authorization for Agents Agent authentication is fundamentally different from human authentication. You cannot send an agent through an OAuth consent screen or ask it to solve a CAPTCHA. ### API Keys with Scoped Permissions The simplest pattern: generate API keys with fine-grained permissions. A "read-only CRM" key, a "manage invoices" key, an "admin" key. Agents should use the least-privileged key for their task. Build a key management dashboard where users can create, rotate, and revoke keys with specific permission scopes. ### OAuth 2.0 with Service Accounts For more sophisticated access control, support OAuth 2.0 client credentials flow. This lets agents authenticate as service accounts with defined scopes, without user interaction. The service account model works well when an organization wants one AI agent to act on behalf of the company rather than impersonating a specific user. ### Delegated Access (Agent Acting as User) Sometimes an agent needs to act on behalf of a specific user (e.g., an AI assistant managing a sales rep's pipeline). Support OAuth 2.0 authorization code flow with long-lived refresh tokens. The user authorizes the agent once, and the agent uses refresh tokens to maintain access. Set reasonable token lifetimes (30 to 90 days) and notify users when tokens are about to expire. ### Rate Limiting and Abuse Prevention Agents can make requests much faster than humans. Set rate limits that are generous enough for legitimate agent use (100 to 1000 requests per minute depending on the endpoint) but protect against runaway agents. Return rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset) so agents can self-throttle. Return 429 with a Retry-After header for clear retry timing. Build [MCP server support](/blog/how-to-build-custom-mcp-servers) alongside your REST API. MCP is becoming the standard protocol for agent-to-tool communication, and supporting it makes your product immediately compatible with every major AI agent framework. ![Security infrastructure and authentication systems for AI agent access control](https://images.unsplash.com/photo-1563986768609-322da13575f2?w=800&q=80) ## Building MCP Servers for Your Product The Model Context Protocol (MCP) is the emerging standard for AI agents to interact with external tools and data sources. If you build one integration for agents, make it an MCP server. ### MCP Basics An MCP server exposes your product's functionality as "tools" that AI agents can call. Each tool has a name, description, input schema (JSON Schema), and output schema. The agent's LLM reads the tool descriptions, decides which tool to use, constructs the input, and processes the output. Your MCP server handles the actual execution. ### Designing Good Tool Definitions The quality of your tool definitions directly impacts how well agents use your product. Each tool needs: - A clear, specific name: create_invoice is better than invoice_action - A description that explains when to use this tool and when not to: "Create a new invoice for a customer. Use this when you need to bill a customer for completed work. Do not use for recurring subscriptions, use create_subscription instead." - Input parameters with descriptions, types, and constraints: "customer_id (required, string): The unique identifier of the customer. Find this using the search_customers tool." - Examples of valid inputs and expected outputs ### Granularity Strike the right balance between too few and too many tools. Too few (a single "do_anything" tool) gives the agent no guidance. Too many (100+ tools) overwhelm the LLM's context window and reduce accuracy. Aim for 10 to 30 tools that cover your product's core functionality. Group related actions logically and provide a "list capabilities" tool that helps agents discover what is available. ### Error Handling in MCP MCP tools should return structured error responses that agents can act on. Include an error code, a human-readable message, and suggestions for recovery. "Invalid customer_id. Use search_customers to find the correct ID" is infinitely more useful than "Bad request" for an agent trying to recover from an error. ## Data Formats and Response Design How you format API responses matters more for agents than for human developers. Agents parse responses programmatically and use the data in subsequent reasoning steps. ### Consistent Response Structure Every endpoint should return the same top-level structure. A "data" field for the payload, a "meta" field for pagination and metadata, and an "errors" field for error details. Never mix response formats between endpoints. An agent that learns your response structure for one endpoint should be able to predict the structure for all endpoints. ### Include Related Data Agents making multiple API calls to assemble related data waste tokens and time. Use compound documents or sideloading (inspired by JSON:API) to include related resources in a single response. When returning an invoice, include the customer details, line items, and payment status rather than requiring three separate API calls. ### Timestamps and Formatting Use ISO 8601 for all dates and times. Include timezone information. Use consistent number formatting (no locale-specific formatting). Return monetary values as integers in the smallest currency unit (cents for USD) to avoid floating-point issues. These seem minor, but inconsistent formatting causes parsing errors that are hard for agents to diagnose. ### Filtering and Querying Support rich filtering so agents can fetch exactly what they need. A query like GET /invoices?status=overdue&customer_id=cust_123&created_after=2028-01-01 is much more efficient than fetching all invoices and filtering client-side. The more specific the query support, the fewer API calls agents need to make. ### Webhooks for Event-Driven Agents Not all agent workflows are request-response. Some agents need to react to events: a new customer signs up, a payment fails, a project status changes. Support webhook subscriptions so agents can register for specific events and receive real-time notifications. Include the complete event payload in the webhook body so agents do not need a follow-up API call to get details. ## Testing and Monitoring Agent Usage You cannot improve what you do not measure. Agent usage patterns differ from human usage and require specific monitoring. ### Agent-Specific Analytics Track which API endpoints agents call most frequently, which tool definitions drive the most usage, which errors agents encounter most often, and which sequences of calls are most common. This data reveals optimization opportunities: if agents frequently call three endpoints in sequence, consider adding a compound endpoint that handles all three in one call. ### Sandbox Environments Provide a sandbox environment where agent developers can test integrations without affecting production data. The sandbox should mirror production behavior exactly, including error responses and rate limits. Stripe's test mode is the gold standard here. Pre-populate the sandbox with realistic sample data so agents have something to work with during development. ### Agent Developer Documentation Create documentation specifically for agent developers, separate from your human-facing docs. Include common agent workflows (step-by-step sequences of API calls for typical tasks), MCP server setup instructions, authentication guides for service accounts, and known limitations or gotchas. ### Monitoring for Abuse Agents can be misconfigured or malicious. Monitor for unusual patterns: thousands of requests per minute from a single key, sequential enumeration of IDs (suggesting data scraping), or repeated writes with different data (suggesting fuzzing). Build automated alerts and circuit breakers that throttle or block suspicious agent activity. ![Developer monitoring AI agent API usage and performance metrics in code editor](https://images.unsplash.com/photo-1555949963-ff9fe0c870eb?w=800&q=80) ## Making Your Product Agent-Ready: A Practical Roadmap You do not need to rebuild your entire API to support agents. Follow this incremental roadmap. ### Phase 1: Foundations (2 to 4 Weeks) - Publish a comprehensive OpenAPI specification for your existing API - Add structured error responses with error codes and suggestions - Implement idempotency keys for all write endpoints - Add rate limit headers to all responses - Create a service account authentication flow ### Phase 2: MCP Server (2 to 3 Weeks) - Build an MCP server exposing your top 10 to 15 API actions as tools - Write detailed tool descriptions with usage guidance and examples - Test with Claude, GPT, and at least one open-source agent framework - Publish the MCP server configuration for easy installation ### Phase 3: Agent Developer Experience (2 to 4 Weeks) - Create a sandbox environment with sample data - Write agent-specific documentation with workflow examples - Build analytics tracking for agent usage patterns - Launch an agent developer program or marketplace listing ### Phase 4: Advanced Features (Ongoing) - Add webhook subscriptions for event-driven agent workflows - Build compound endpoints for common agent call sequences - Implement A2A (Agent-to-Agent) protocol support for agent-to-agent communication - Create an agent app store or marketplace for your platform The [MCP and A2A protocols](/blog/a2a-vs-mcp-agent-communication-protocols) are evolving rapidly. Stay current with both standards and update your implementations as new versions release. Building software for AI agents is no longer optional for B2B SaaS products. The companies that make their products agent-friendly today will capture a growing segment of automated workflows. The companies that do not will find their products bypassed as agents choose more accessible alternatives. [Book a free strategy call](/get-started) to discuss making your product agent-ready, building MCP servers, or designing agent-friendly APIs. --- *Originally published on [Kanopy Labs](https://kanopylabs.com/blog/how-to-build-software-for-ai-agents)*