05 — About
This is an enterprise-pattern proof, not a claim that a complete commercial migration platform is finished. It demonstrates the full governance and execution loop on bounded, reproducible data and measures planning scale, data movement and operational load independently.
B.01Inspiration
Data migration projects often fail before the first byte moves. Teams cannot reliably answer what depends on a table, where sensitive data flows, which transformations will break, whether a fix was already learned, or whether a cutover approval applies to the current plan. We wanted to explore a different kind of automation: not a chatbot that recommends migration steps, but a governed fleet that can perform real work while making every autonomous action durable, bounded and auditable.
The hardest challenge was separating useful model reasoning from production authority. Legacy text is necessary for lineage and risk analysis but cannot be trusted as policy input. The project therefore passes only structured identity and resource context into deterministic authorization.
Agentic systems become more credible when their limits are architectural, not merely prompted. Models are strongest at interpreting fragmented evidence, generating structured hypotheses and explaining decisions. They should not own the truth of a checksum, the legality of a state transition, or the authority to approve production cutover.
B.02Difference
Models explain and propose; deterministic services own permissions, state transitions, validation truth and approval integrity.
Versioned AgentCards reduce orchestration coupling and support cross-department providers.
Investigation, memory recall, cataloged remediation and revalidation are designed into the workflow.
A clean-estate gate fails if a second database engine leaks source-specific logic into the core agent directory.
Deployed, live-executed, implemented, tested and planned are intentionally different states.
Metadata planning scale, actual rows moved and concurrent operational load are measured separately.
B.03Proved
Deployed the expected nine-service Cloud Run topology.
Completed a real REQUESTED-to-COMPLETE migration using Cloud SQL, Cloud Run Jobs, BigQuery, deterministic validation, human approval, cutover and monitoring.
Captured a separate 73,595-row, 31.4 MB data-plane measurement.
Benchmarked 20,000 control-plane migration definitions without misrepresenting them as completed data migrations.
Blocked unsafe raw-PII access through a policy engine isolated from free-text legacy content.
Implemented memory-assisted recovery while keeping remediation selection and revalidation deterministic.
B.04Roadmap
Near term
Typed HTTP dispatch for every remaining agent
Each stage runs under its own IAM identity and appears in one propagated trace.
Scale term
Partitioned, checkpointed data plane with backpressure
Larger measured row and byte tiers complete with restart evidence and bounded cost.
Platform term
Signed Migration Packs and adapter certification
A third source family onboards without core-agent changes.
Enterprise term
Tenant isolation, residency, retention and compliance exports
Independent policy and audit evidence exists for every tenant and connector.
B.05Team
Built for the All Things Agentic Hackathon, Fortified Enterprise Fleet category.
Cloud, network and infrastructure engineering
Built and hardened the Google Cloud footprint the control tower runs on — the nine-service Cloud Run topology, the private networking and service identities, the Pub/Sub event backbone, and the Terraform that makes all of it reproducible.
Discovery and frontend development
Built the discovery path that turns a fragmented legacy estate into catalogued evidence, and the operator console that presents it — the workspace where runs, lineage, approvals and evidence are actually read.
B.06Closing
Migration Control Tower demonstrates how agentic reasoning can accelerate discovery, planning and recovery while deterministic controls preserve enterprise trust. Every action is scoped, every state transition is durable, every validation result is reproducible, and production cutover remains under human authority.
Next
Get in touch
Contact