Problem Details (RFC 9457) is an IETF standard defining a consistent JSON or XML format for HTTP API error responses, providing a type URI, a human-readable title, the HTTP status code, and a detailed description to improve client-side error handling. It supersedes RFC 7807, adding explicit support for multiple problems in a single response and standardizing the use of the application/problem+json and application/problem+xml media types. This format enables dynamic invocation by AI agents, allowing them to programmatically parse and reason about API failures.
Glossary
Problem Details (RFC 9457)

What is Problem Details (RFC 9457)?
Problem Details for HTTP APIs, defined by RFC 9457, is a standardized machine-readable format for reporting errors from web services.
The standard mandates a core set of members—type, title, status, and detail—while allowing APIs to extend the object with custom type definitions for domain-specific context. This structure is crucial for schema validation and orchestration layer design, as it provides a predictable error model that tools can rely on for recursive error correction. By offering a machine-readable alternative to plain text error messages, it facilitates robust automated API testing suites and reliable integration within agentic cognitive architectures.
Core Members of a Problem Details Object
RFC 9457 (formerly RFC 7807) defines a standardized JSON or XML format for HTTP API errors. These core members provide machine-readable details to improve client error handling.
type
A URI reference that identifies the problem type. It is the primary identifier for the error category and should be dereferenceable to human-readable documentation.
- Absolute URI: Recommended for global types (e.g.,
https://api.example.com/errors/out-of-stock). - Relative URI: Can be used for types specific to the local API context.
- This member is optional, but its use is strongly encouraged to allow clients to programmatically identify the error.
title
A short, human-readable summary of the problem type. It should be consistent for the same type and should not change from occurrence to occurrence.
- Example: "Out of Stock", "Validation Failed", "Access Denied".
- This is a recommended member. It provides a concise, static title for the error category, distinct from the instance-specific
detail.
status
The HTTP status code generated by the origin server for this occurrence of the problem. It mirrors the status code in the HTTP response header.
- Example:
404,422,500. - This member is optional but highly useful, as it allows the problem detail to be processed independently of the HTTP headers.
detail
A human-readable explanation specific to this occurrence of the problem. It provides instance-specific details that the title does not.
- Example: "The product with SKU 'ABC-123' is no longer available in the requested size 'Large'."
- This is an optional member. It is the primary place for actionable, contextual error information for end-users or developers.
instance
A URI reference that identifies the specific occurrence of the problem. It may point to a log entry, a specific resource, or a unique error ID.
- Example:
"/log/errors/550e8400-e29b-41d4-a716-446655440000" - This is an optional member. It is extremely valuable for debugging, allowing support teams to locate the exact error event in backend systems.
Extension Members
Problem Details objects may include additional members beyond the core five to provide further machine-readable context.
- Namespace: Extension members should avoid conflict with core members and other extensions. Using a vendor-specific prefix (e.g.,
accountId,invalidParams) is a common practice. - Use Case: Common extensions include
invalidParamsfor validation error details orbalancefor a financial API error. - The standard explicitly allows for this extensibility to support domain-specific error information.
Frequently Asked Questions
Problem Details for HTTP APIs (RFC 9457) is a standardized format for reporting machine-readable error information. These FAQs address its core concepts, implementation, and role in AI agent integrations.
Problem Details for HTTP APIs, defined by RFC 9457 (which obsoletes RFC 7807), is a standardized JSON or XML format for returning machine-readable error information from HTTP APIs. It works by providing a consistent response structure that includes a type URI (identifying the problem), a title (a short human-readable summary), the HTTP status code, a detailed detail message, and an optional instance URI pinpointing the specific occurrence. This structure allows both automated clients, like AI agents, and human developers to understand and handle errors programmatically, moving beyond opaque status codes and plain text messages.
For example, a 403 Forbidden error might return:
json{ "type": "https://api.example.com/errors/insufficient-scope", "title": "Insufficient Permissions", "status": 403, "detail": "Your token has the 'read' scope but this endpoint requires 'write'.", "instance": "/v1/users/123" }
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Related Terms
Problem Details (RFC 9457) is a core component of robust API integration. These related concepts define the ecosystem of standards, tools, and practices for building reliable, machine-readable interfaces.
OpenAPI Specification
The OpenAPI Specification (OAS) is the industry-standard, machine-readable format for describing RESTful APIs. It defines endpoints, operations, parameters, authentication methods, and data models.
- Serves as the foundational API contract between providers and consumers.
- Enables automated code generation for client SDKs and server stubs.
- Provides the structural blueprint that AI agents parse during schema ingestion to understand available tools.
JSON Schema
JSON Schema is a declarative language for annotating, validating, and documenting the structure of JSON data. It defines allowed data types, formats, constraints, and relationships.
- Used within OpenAPI to define the precise shape of request and response payloads.
- Enforces schema validation to ensure data correctness before and after API calls.
- Critical for providing structured output guarantees from AI models, ensuring generated parameters are type-safe.
Contract Testing
Contract testing is a methodology for verifying that two services (e.g., an AI agent and a backend API) adhere to a shared interface definition. It tests the contract, not the implementation.
- Ensures that the consumer's expectations, derived from the API schema, match the provider's actual responses.
- Catches breaking changes before deployment, supporting safe schema evolution.
- A key practice for maintaining integration reliability in API First Design.
Dynamic Invocation
Dynamic invocation is the runtime construction and execution of API calls based on a machine-readable schema, rather than using hardcoded client libraries.
- The core mechanism enabling AI agents to call arbitrary external tools described by an OpenAPI spec.
- Involves programmatically resolving endpoints, serializing parameters per JSON Schema, and handling the response.
- Requires robust error handling and retry logic to manage API failures gracefully.
API Gateway Integration
API gateway integration involves configuring an API gateway to use an OpenAPI schema as its source of truth for routing, security, and validation.
- The gateway can perform automatic request/response validation against the schema, rejecting malformed calls.
- Enforces authentication, rate limiting, and transforms requests, acting as a zero-trust API gateway for agent traffic.
- Often the point where Problem Details error responses are standardized and returned to the client.
AsyncAPI
AsyncAPI is an open-source specification, modeled on OpenAPI, for describing asynchronous, event-driven APIs that use protocols like Kafka, MQTT, or WebSockets.
- Defines message formats, channels, and operations for event producers and consumers.
- Enables the same machine-readable tooling and code generation for event-based architectures.
- Extends the concept of a machine-readable contract beyond request/response to pub/sub and streaming integrations.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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