AI / AUTOMATION
A production-minded workflow model for internal operations, routing, and monitoring across distributed systems.
CONTEXT
This reference model explores how service requests, events, and decision loops can be coordinated across teams and systems without creating operational blind spots.
CHALLENGE
- Route work reliably between services and teams
- Keep decisions visible without adding operational sprawl
- Provide enough traceability for escalation and recovery
OUTCOMES
- One operational view across intake, routing, and execution state
- Faster incident triage through explicit workflow context
- Clearer separation between orchestration logic and runtime execution
ARCHITECTURE
- 01
WORK INTAKE
Accepts requests and events from internal systems.
- 02
ORCHESTRATOR
Tracks workflow state and chooses the next action.
- 03
QUEUE
Buffers asynchronous work for independent workers.
- 04
WORKERS + TELEMETRY
Execute tasks and expose state for recovery.
DECISIONS
ProblemOne team owned the workflow and another owned the runtime, which created blind spots.
DecisionModel workflow state explicitly and expose it in a shared operational UI.
ReasoningOwners need to see the same state transitions regardless of whether a request is in-flight, blocked, or recovered.
ProblemThe system needed structured automation without hard-coding business rules in every component.
DecisionSeparate orchestration logic from execution handlers and keep routing rules explicit.
ReasoningThis limits accidental coupling and makes it easier to adapt the workflow when operating constraints change.
STACK
- Next.js
- NestJS
- Redis
- PostgreSQL
- Kafka
- OpenTelemetry
- Docker