# Agent events

> Agent events - On a deployment's Agent events tab, you can view Management and Monitoring events.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:10.023202+00:00` (UTC).

## Primary page

- [Agent events](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-agent-events.html): Full documentation for this topic (HTML).

## Sections on this page

- [Management events](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-agent-events.html#management-events): In-page section heading.
- [Monitoring events](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-agent-events.html#monitoring-events): In-page section heading.
- [Rate limits for the deployments API](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-agent-events.html#rate-limits-for-the-deployments-api): In-page section heading.
- [Enable agent event log for external models](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-agent-events.html#enable-agent-event-log-for-external-models): In-page section heading.

## Related documentation

- [NextGen UI documentation](https://docs.datarobot.com/en/docs/workbench/index.html): Linked from this page.
- [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html): Linked from this page.
- [Activity log](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/index.html): Linked from this page.
- [spooler configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html): Linked from this page.
- [Installation and configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html): Linked from this page.

## Documentation content

On a deployment's Activity log > Agent events tab, you can view Management events (e.g., deployment actions) and Monitoring events (e.g., spooler channel and rate limit events). Monitoring events can help you quickly diagnose MLOps agent issues. The spooler channel error events can help you diagnose and fix [spooler configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html) issues. The rate limit enforcement events can help you identify if service health stats, data drift values, or accuracy values aren't updating because you exceeded the API request rate limit.

To view the agent events log, navigate to the Activity log > Agent events tab. The most recent events appear at the top of the list.

Each event shows the time it occurred, a description, and an icon indicating its status:

| Status icon | Description |
| --- | --- |
| Green / Passing | No action needed. |
| Yellow / At risk | Concerns found, but no immediate action needed; continue monitoring. |
| Red / Failing | Immediate action needed. |
| Gray / Informational | Details a deployment action (e.g., deployment launch has started). |
| Gray / Unknown | Unknown. |

To interact with the Agent events log, configure the Categories filter to limit the list to Management events (e.g., deployment actions) or Monitoring events (e.g., spooler channel and rate limit events).

## Management events

Management events represent deployment actions and can help you review the status of your deployment and the health of the management agent. Currently, the following events can appear as management events:

| Event type | Description |
| --- | --- |
| Deployment launch | Reports deployment launch status, notifying you when launch starts and if it succeeds or fails. |
| Deployment shutdown | Reports deployment shutdown status, notifying you when shutdown starts and if it succeeds or fails. |
| Model replacement | Reports model replacement status, notifying you when replacement starts and if it succeeds or fails. |
| Prediction request | Reports model prediction status, notifying you if a prediction request fails. |
| Management agent health | Reports changes in the health of the management agent implementation for this deployment. |
| Service health has changed | Reports changes in the service health of the deployed model. |
| Data drift has changed | Reports changes in the data drift status of the deployed model. |
| Accuracy has changed | Reports changes in the accuracy status of the deployed model. |

## Monitoring events

Monitoring events can help you diagnose and fix agent issues. Currently, the following events can appear as monitoring events:

| Event | Description |
| --- | --- |
| Monitoring spooler channel | Diagnose spooler configuration issues to troubleshoot them. |
| Rate limit was enforced | Identify when an operation exceeds API request rate limits, resulting in updates to service health stats, data drift calculations, or accuracy calculations stalling. This event reports how long the affected operation is suspended. Rate limits are applied per deployment, per operation. |

### Rate limits for the deployments API

| Operation | Endpoint (POST) | Limit |
| --- | --- | --- |
| Submit Metrics (Service Health) | api/v2/deployments/<id>/predictionRequests/fromJSON/ | 1M requests / hour |
| Submit Prediction Results (Data Drift) | api/v2/deployments/<id>/predictionInputs/fromJSON/ | 1M requests / hour |
| Submit Actuals (Accuracy) | api/v2/deployments/<id>/actuals/fromJSON/ | 40 requests / second |

### Enable agent event log for external models

To view Monitoring events, you must provide a `predictionEnvironmentID` in the agent configuration file ( `conf\mlops.agent.conf.yaml`) as shown below. If you haven't already installed and configured the MLOps agent, see the [Installation and configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html) guide.

**MLOps Agent configuration file example**

| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # This file contains configuration for the MLOps agent # URL to the DataRobot MLOps service mlopsUrl: "https://<MLOPS_HOST>" # DataRobot API token apiToken: "<MLOPS_API_TOKEN>" # Execute the agent once, then exit runOnce: false # When dryrun mode is true, do not report the metrics to MLOps service dryRun: false # When verifySSL is true, SSL certification validation will be performed when # connecting to MLOps DataRobot. When verifySSL is false, these checks are skipped. # Note: It is highly recommended to keep this config variable as true. verifySSL: true # Path to write agent stats statsPath: "/tmp/tracking-agent-stats.json" # Prediction Environment served by this agent. # Events and errors not specific to a single deployment are reported against this Prediction Environment. predictionEnvironmentId: "<PE_ID_FROM_DATAROBOT_UI>" # Number of times the agent will retry sending a request to the MLOps service on failure. httpRetry: 3 # Http client timeout in milliseconds (30sec timeout) httpTimeout: 30000 # Number of concurrent http request, default=1 -> synchronous mode; > 1 -> asynchronous httpConcurrentRequest: 10 # Number of HTTP Connections to establish with the MLOps service, Default: 1 numMLOpsConnections: 1 # Comment out and configure the lines below for the spooler type(s) you are using. # Note: the spooler configuration must match that used by the MLOps library. # Note: Spoolers must be set up before using them. # - For the filesystem spooler, create the directory that will be used. # - For the SQS spooler, create the queue. # - For the PubSub spooler, create the project and topic. # - For the Kafka spooler, create the topic. channelConfigs: - type: "FS_SPOOL" details: {name: "filesystem", directory: "/tmp/ta"} # - type: "SQS_SPOOL" # details: {name: "sqs", queueUrl: "your SQS queue URL", queueName: "<your AWS SQS queue name>"} # - type: "RABBITMQ_SPOOL" # details: {name: "rabbit", queueName: <your rabbitmq queue name>, queueUrl: "amqp://<ip address>", # caCertificatePath: "<path_to_ca_certificate>", # certificatePath: "<path_to_client_certificate>", # keyfilePath: "<path_to_key_file>"} # - type: "PUBSUB_SPOOL" # details: {name: "pubsub", projectId: <your project ID>, topicName: <your topic name>, subscriptionName: <your sub name>} # - type: "KAFKA_SPOOL" # details: {name: "kafka", topicName: "<your topic name>", bootstrapServers: "<ip address 1>,<ip address 2>,..."} # The number of threads that the agent will launch to process data records. agentThreadPoolSize: 4 # The maximum number of records each thread will process per fetchNewDataFreq interval. agentMaxRecordsTask: 100 # Maximum number of records to aggregate before sending to DataRobot MLOps agentMaxAggregatedRecords: 500 # A timeout for pending records before aggregating and submitting agentPendingRecordsTimeoutMs: 5000 |