Deploy an AI agent to make it active and available for task execution
Deploy an agent to production. This validates the configuration and makes the agent available for invocation.Documentation Index
Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt
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id: Agent identifiername: Agent display namestatus: Updated status (ACTIVE or INACTIVE)deployment_type: Deployment infrastructuremodel_provider: AI model providermodel_name: Specific model versionframework: Agent framework usedorganization_id: UUID of the organization that owns this agentdescription: Auto-generated from instructions if not previously sethas_pending_changes: Indicates whether there are unpublished changesversion: Current version number of the agentdescription is auto-generated from instructions if not previously sethas_pending_changes returns to false after successful deploymentACTIVE status indicates the agent is ready to handle tasksAPI Key for authentication
Successful Response
Enumeration of the agent delegation end strategies.
Attributes: ReturnToStart: when last agent is finished and about to announce "finish" it will summarize and return to the first agent. FinishWithLast: finish at the last agent.
return-to-start, finish-with-last serverless, container personal, organizational Enumeration of possible agent statuses.
Attributes: DRAFT: Agent is in a draft state. ACTIVE: Agent is active and operational. INACTIVE: Agent is inactive and not operational.
DRAFT, ACTIVE, INACTIVE Enumeration of the agent types.
Attributes: Manager: marks the agent as a Managing agent. Regular: marks the agent as a regular agent. A2A: marks the agent as an external agent used via A2A protocol. Curl: marks the agent as an external agent used via a CURL. Orchestration: marks the agent as an Orchestration object.
manager, regular, a2a, curl, orchestration Enumeration of the agent delegation types.
Attributes: Router: Marks the agent as a router agent - xpanderAI's LLM will decide which sub-agent to trigger. Sequence: Marks the agent as a sequence agent - sub-agents will delegate to other sub-agents.
router, sequence Enumeration of the agent delegation memory strategies.
Attributes: Full: The memory object will be passed completely between agents. Summarization: Between each sub-agent delegation, a summarization will occur, and a new thread will be created for each agent. OriginalInput: the sub agent will get the initial task with a fresh memory thread
full, summarization, original-input openai, nim, amazon_bedrock, azure_ai_foundary, huggingFace, friendlyAI, anthropic, gemini, fireworks, google_ai_studio, helicone, bytedance, tzafon_lightcone, open_router, nebius, cloudflare_ai_gw low, medium, high, xhigh text, markdown, json, voice xpander, custom Configuration for event-based notifications.
Attributes: on_success: Notifications to send when an operation succeeds. Maps notification types to a list of notification configurations. on_error: Notifications to send when an operation fails. Maps notification types to a list of notification configurations.
Configuration object for task-level execution strategies.
This model groups optional strategy configurations that control how a task is executed and managed over time, including retries, iterative execution, stopping conditions, and daily run limits.
Attributes: retry_strategy: Optional retry policy configuration that defines how the task should behave when execution fails (e.g., max attempts, backoff rules).
iterative_strategy:
Optional iterative execution configuration for tasks that may run in
repeated cycles/steps until completion or a stop condition is met.
stop_strategy:
Optional stopping policy configuration that defines when the task
should stop running (e.g., timeout, max iterations, success criteria).
max_runs_per_day:
Optional limit on how many times the task is allowed to run within a
24-hour period. If not set, no explicit daily limit is enforced.
agentic_context_enabled:
if agentic memory is enabled and accesible to the executor.