Automation
AI Project Operations System
An internal platform for orchestrating engineering workflows: task qualification and decomposition, reconciling data across systems, reporting.
Context
An engineering organization with a high coordination load: task breakdown, status collection, routing and reporting across several systems.
Problem
Managers spent hours on manual coordination, and context was smeared across trackers, conversations and repositories, so statuses went stale faster than they were collected.
Constraints
High-cost-of-error decisions stay with humans; transparency and audit are mandatory at every step.
Architecture
An event-driven orchestrator: task qualification → decomposition → reconciling data across systems → context and reporting aggregation.
AI layer
Building task structures and report drafts; the strong model is used only on critical steps, the rest on a cheap one.
Event model
Changes in a tracker or repository become events that trigger synchronization and a context rebuild — with no nightly runs.
Integrations
Task trackers, repositories and communication channels behind a normalized layer shared by all workflow steps.
Automation flows
Automatic decomposition, status collection, report preparation, role-based routing of work.
Infrastructure
Event queues, idempotency, materialized workflow state per task.
Observability
Tracing of steps, cost and model decisions; an audit of exactly what was auto-generated and on what basis.
Results
Fewer manual coordination roles, higher workflow throughput, statuses no longer going stale.
Lessons
Coordination is a stateful workflow; AI speeds it up but does not remove human control over expensive decisions.