Agentforce Service Errors

This reference contains errors from Agentforce platform services, organized by service and error code. Documentation for each error includes possible causes, troubleshooting steps, and if necessary, when to reach out to Salesforce Customer Support and what information to provide.

Service	Description
Agent Service Reasoner Errors	The Agent Service Reasoner manages agent conversations on two surfaces: the multi-agent orchestration layer, where connected subagents collaborate to complete complex tasks, and the single-agent reasoning endpoint, where individual agents process requests from users and calling applications. Errors from this service typically indicate that a request was malformed or missing required context, that the requested agent or session couldn’t be found, that the caller lacked permission, or that a language model call timed out, was rate limited, or encountered a service-side failure.
Agentforce Data Library Service Errors	The Agentforce Data Library service manages data libraries, sources, retrievers, and file uploads for use in Agentforce agents. Errors from this service typically indicate that a requested resource wasn’t found, that a permission or prerequisite is missing, that a request field failed validation, or that a provisioning operation encountered a temporary failure.
Authentication Service Errors	The Agentforce authentication service manages org provisioning and the credentials that allow Agentforce and Einstein AI features to operate. Errors from this service typically indicate that an org couldn’t be provisioned, that authentication credentials couldn’t be validated, or that a platform dependency the service relies on was temporarily unavailable.
Bring Your Own Node Service Errors	The Bring Your Own Node (BYON) service runs custom code nodes within Agentforce agent workflows. When your agent runs, the reasoning engine walks a graph of steps to fulfill a request. Each node in that graph represents a discrete operation. The reasoning engine determines which node to call, assembles the inputs, and calls your code to execute it. Errors from this service typically indicate that the requested node wasn’t found, that the request payload failed validation, that the caller lacked permission to execute the node, or that the execution timed out or was cancelled before completing.
Einstein Compute Service Errors	The Einstein Compute service schedules and runs AI model training and data processing jobs that power Agentforce and other generative AI features. Errors from this service typically indicate that a job request was invalid or referenced a resource that doesn’t exist, that the platform’s compute infrastructure was temporarily unavailable or at capacity, or that an internal dependency encountered an unexpected failure.
General Agentforce Service Errors	These error codes can be returned by any Agentforce service. They represent platform-level conditions that apply across services, such as rate limiting, temporary unavailability, and unexpected system failures.
Interactive Data Science Service Errors	The Interactive Data Science service is the backend infrastructure for Model Studio, where admins and data scientists create projects, connect data sources, and deploy custom models into Agentforce and Einstein AI features. Errors from this service typically indicate that a project request was invalid or referenced a resource that doesn’t exist, that the org isn’t enabled for the requested capability, or that a platform dependency the service relies on was temporarily unavailable.
Language Detection Service Errors	The language detection service identifies the language of text submitted through Agentforce and Einstein AI features. Errors from this service typically indicate that the input text was too short or the wrong type, that required parameters were missing, or that the pipeline configuration connecting language detection to other steps was incorrect.
ML Lake Service Errors	The ML Lake service manages data storage access for Einstein AI pipeline jobs. Errors from this service typically indicate that the request or org identifier was invalid, that credentials couldn’t be issued or aren’t authorized, or that the service was temporarily unavailable.
Model Builder Errors	The Model Builder service trains predictive AI models on your Data 360 data. Errors from this service typically indicate that a training job request was invalid or missing required parameters, that a job or model artifact couldn’t be found, or that a platform dependency the service relies on was temporarily unavailable.
Prediction Service Errors	The prediction service routes AI inference requests to the appropriate models. Errors from this service typically indicate that a request failed schema validation or violated a business rule, that the requested application type or model version isn’t supported in the current environment, that authentication failed, or that a required model configuration couldn’t be found on the platform.