Control-M + Amazon Bedrock AgentCore

Amazon Bedrock AgentCore workflow starts before Control-M ever triggers an agent — and it can fail when something upstream breaks. An Amazon S3 dataset arrives incomplete. An AWS Glue transformation finishes two hours late. Control-M orchestrates the full chain: Amazon S3 data arrival, AWS Glue transformation, Amazon Bedrock AgentCore execution, and downstream delivery.

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common workflow issues

Does this sound like your week?

These aren’t edge cases. They’re the normal operating conditions for teams running Amazon Bedrock AgentCore agent workflows across multiple tools. Here’s how Control‑M handles each one.

UPSTREAM DATA DELAYS

Your S3 dataset arrived at 02:00. AgentCore is waiting for complete inputs.

Control-M uses file arrival conditions and upstream dependencies to coordinate agent execution with required data preparation. If the expected Amazon S3 delivery is missing or late, dependent jobs remain on hold, preventing premature execution against incomplete inputs.

GLUE JOB FAILURE

AWS Glue failed overnight. Your AgentCore workflow still needs refreshed customer data.

Control-M evaluates the AWS Glue job's completion status before releasing the dependent AgentCore invocation. A failed transformation blocks downstream execution, triggers configured alerts, and supports controlled recovery, preventing stale data from entering the agent workflow.

API THROTTLING RISK

AgentCore returns ThrottlingException. Your scheduled reporting handoff is now at risk.

Control-M can apply configured retry intervals and attempt limits to failed agent-trigger jobs, independent of AgentCore's own throttling behavior. Persistent API errors trigger escalation, while downstream dependencies remain blocked until successful completion, reducing manual intervention and avoiding uncontrolled repeat requests.

DOWNSTREAM DELIVERY FAILURE

Your AgentCore invocation succeeded. The Redshift reporting load never started.

Control-M tracks the invocation job and downstream delivery dependencies within one workflow. Configured success conditions release the Redshift load, while failures stop dependent processing and generate alerts, helping teams complete the business process rather than merely the API call.

SLA DEADLINE RISK

Your 06:00 AgentCore output is late. The analytics deadline is approaching.

Control-M monitors the complete workflow against defined service deadlines and provides alerts when completion is at risk. Teams can identify delayed dependencies, prioritize recovery, and coordinate downstream processing before missed delivery windows affect business operations.

Control‑M + Amazon Bedrock AgentCore

workload.types	AgentCore agent invocations · agent-driven data enrichment · agentic workflows for document classification, summarization, and report generation (executed by the agent, orchestrated by Control-M)
trigger.type	Amazon S3 file arrival · AWS Glue job completion · upstream job exit status · time-based schedule · Control-M event condition · API-initiated workflow
cross_tool.deps	AWS Glue ETL · Amazon S3 delivery · AWS Lambda execution · Apache Airflow DAG completion · AWS Step Functions execution · Amazon Redshift load · downstream file delivery
cloud.platforms	Amazon Web Services · Control-M SaaS · self-hosted Control-M · hybrid enterprise environments
error_handling	Configurable job retries · retry intervals · upstream failure holds · downstream cascade prevention · Control-M handling of AgentCore API errors · SLA alerts · job escalation
throughput	Scheduled batch invocation · parallel Control-M jobs · dependency-driven execution · Control-M workload concurrency management · Control-M retry configuration for throttled invocations
observability	Control-M job status · dependency visualization · job execution history · SLA tracking · AgentCore CloudWatch metrics · AgentCore runtime logs · OpenTelemetry tracing

end-to-end orchestration

One production workflow. Every tool in the stack.

Control‑M orchestrates workflows across Amazon Bedrock AgentCore, AWS Glue, Amazon S3, AWS Lambda, Apache Airflow, and cloud services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.

Cross-tool dependency: Amazon S3 → AWS Glue → Amazon Bedrock AgentCore → Amazon Redshift
Data-aware triggers: S3 file arrival, API event, AWS Glue completion, upstream job result

View all integrations

Amazon Bedrock AgentCore	Agent Runtime invocation · job dependencies · execution status handling · retry orchestration
Amazon S3	File arrival detection · input dependency coordination · data delivery
AWS Glue	ETL job execution · completion tracking · downstream release
AWS Lambda	Function invocation · execution monitoring · workflow coordination
Apache Airflow	DAG triggering · status tracking · cross-platform dependency management
AWS Step Functions	State machine execution · completion tracking · downstream workflow coordination
Amazon Redshift	Data load orchestration · SQL job dependencies · reporting handoff

airflow coexistance

Control‑M doesn’t replace your Airflow DAGs. It runs the layer above them.

The objection is common: “We’re already on Airflow.” The issue isn’t what Airflow does – it’s what happens before and after Airflow runs. That’s where pipelines actually fail.

Airflow manages its DAG. Control-M manages everything surrounding it.

airflow handles

DAG-level orchestration inside the data pipeline

DAG-level task orchestration within data pipelines
Python operators, sensors, and task dependencies
Execution graph for jobs that run inside your pipeline
Manages retries within a single DAG context

control-m adds

The coordination layer around your DAGs

Coordination layer around DAGs — triggers Airflow based on upstream conditions: file arrivals, API events, other tool completions
Tracks each DAG’s SLA contribution across the full end-to-end workflow, not just its own routine
Manages failure recovery when upstream dependencies fail before Airflow even starts
Existing DAGs don’t need to be rewritten or migrated

MONITOR AI WORKFLOWS

Monitor Amazon Bedrock AgentCore workflows from start to finish

Amazon Bedrock AgentCore provides runtime observability, but individual agent metrics don't show whether upstream datasets arrived or downstream processes completed. Control-M provides a unified view of execution across the entire data pipeline, helping teams identify failures and delays through:

Agent execution status and history
Upstream and downstream job dependencies
Cross-platform workflow execution visibility
Configurable failure alerts and notifications
Job duration and runtime trends

SLA ASSURANCE

Keep AgentCore workflows aligned with critical delivery deadlines

AgentCore runtime telemetry doesn't track business deadlines spanning multiple platforms. Control-M monitors the complete workflow against defined service levels, helping teams identify delayed dependencies, anticipate missed deadlines, and coordinate recovery before downstream analytics and operational commitments are affected through:

End-to-end workflow SLA monitoring
Predictive SLA breach alerts
Critical-path dependency visibility
Configurable escalation and notifications
Automated downstream job release

Documentation

Integrating Amazon Bedrock AgentCore and Control-M

Bring order to complex workflows

Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.

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