Monitoring
Track the signals that show whether the workflow and its dependencies are operating as expected.
PRODUCTION SUPPORT
Managed automation support
Because “it worked during the demo” is not an operating model. Production automation needs monitoring, maintenance, incident investigation, and deliberate improvement as dependencies and business rules change.
01
AI interprets.
02Software controls.
03People own consequential decisions.
WHO THIS IS FOR
Production workflows depend on moving parts
External services, credentials, data formats, infrastructure, models, and business rules all change. Support keeps those changes visible and manageable.
SCOPE
What managed support can include
The support scope, coverage, response expectations, access, and responsibilities are agreed for the actual workflow.
Incident investigation
Use logs, workflow state, inputs, dependency evidence, and recent changes to determine what failed.
Integration maintenance
Adapt to authentication, schema, API, webhook, and downstream-system changes.
Retry and recovery management
Review failed work, apply safe recovery behavior, and improve exception handling where justified.
Workflow optimization
Prioritize changes to rules, routing, interfaces, performance, cost, and operator experience.
Model tuning where used
Review real inputs, confidence, error patterns, evaluation evidence, and usage cost without treating the model as the whole workflow.
WORKFLOW
From signal to controlled recovery
Support needs enough evidence to distinguish a bad input, integration failure, rule change, model issue, and infrastructure incident.
- 01
Monitoring detects a change
- 02
Affected workflow state is identified
- 03
Inputs and dependencies are inspected
- 04
Safe recovery path is selected
- 05
Failed work is recovered or routed
- 06
Root cause and follow-up are recorded
Not every alert is an incident, and not every failed action should retry automatically. The operating rules are specific to the workflow.
RESPONSIBILITY BOUNDARIES
AI interprets. Software controls.
Models handle ambiguous inputs; human review is placed where judgment, responsibility, or uncertainty matters.
- Track confidence distribution
- Review representative errors
- Evaluate changed inputs
- Tune prompts or models when evidence supports it
- Health checks and alerts
- Workflow state and queues
- Retries and duplicate protection
- Permissions and recovery actions
- Logs and incident evidence
FAILURE CONTROL
Monitoring should match the workflow
Useful signals depend on what the workflow does and what consequence a delay or error creates.
Workflow health
Failures, processing time, retry volume, queue backlog, exceptions, and unusual changes can reveal operational problems.
Dependency health
API errors, credential expiry, infrastructure errors, webhooks, and integration health show where external change is affecting the workflow.
Interpretation health
Where models are used, confidence distribution and reviewed examples can expose changed input or degraded behavior.
HOW WE WORK
A support model shaped around operational responsibility
DapperAgent can potentially operate the workflow, collaborate with internal developers, document it for handoff, or transition operational responsibility.
Baseline
Review architecture, dependencies, access, documentation, known failures, and current monitoring.
Observe
Establish useful workflow, integration, infrastructure, and model signals.
Respond
Investigate and recover according to the agreed support scope and operating permissions.
Improve
Prioritize recurring failure, reliability, maintainability, cost, and workflow changes.
Document or hand over
Keep operating knowledge current and support a planned transition where agreed.
SCOPE AND COST
What affects support scope and cost?
Support is scoped around system criticality, coverage expectations, access, dependencies, volume, and the responsibility DapperAgent is expected to hold.
- Workflow criticality
- Coverage expectations
- Number of dependencies
- Access and environments
- Transaction volume
- Existing observability
- Recovery complexity
- Change frequency
OWNERSHIP
Operate together or prepare for handoff
The operating model can be shaped around DapperAgent, internal developers, or a planned transition. Responsibilities and access are documented rather than assumed.
CLIENT WORK
Real-world visual inputs require ongoing evidence
For a waste-management platform, a model interprets vehicle-camera evidence while configured software rules control how selected event types continue. Real-world visual inputs and platform dependencies change, so the component receives ongoing monitoring, support, and optimization.
Read the monitored event-filtering caseRELATED CAPABILITIES
Continue with the part of the workflow you need
FAQ
Practical questions before you start
Do you support automation built by another team?
Potentially, after a technical and operational review establishes architecture, access, documentation, risks, dependencies, and a safe support boundary.
Can support begin after the original project?
Yes. A baseline review is used to understand the current implementation and agree what can responsibly be supported.
What happens if an integration fails outside working hours?
Coverage, alert routing, automated safeguards, and response expectations must be agreed for the workflow. No universal SLA is assumed.
Can you improve an existing workflow?
Yes. Improvements may address recurring failure, observability, validation, integration reliability, maintainability, cost, or operator experience after the current behavior is understood.
NEXT STEP
Support the workflow as a production system.
Tell us what is running, what it depends on, how failure is detected today, and where operational responsibility currently sits.