Infrastructure
Platform Engineering
The infrastructure on which AI workflows run predictably, observably and at controlled cost: events, observability, token economics, integrations.
An AI system lives only as long as its platform is resilient. Platform engineering is the part on which workflows launch predictably, are debugged from traces, and don’t blow the model bill. Without it, every workflow re-solves infrastructure problems from scratch, each in its own way.
Event-driven architecture
Workflows are triggered by events, not timer polling. Queues, idempotency, retries and state storage are platform properties, not code rewritten in every workflow. Done once and correctly, an individual workflow does its own job instead of fighting duplicates and races.
Observability
Tracing of steps, cost, latency and model decisions is not “logs just in case” — it is the condition for the system being operable at all. Without observability, degradation and overspend are noticed from a complaint or an invoice, i.e. too late and with no way to find the cause.
Token economics
Context budgets, hybrid model routing, caching of stable request parts. Cost is a platform-level engineering metric, like latency: designed up front and attached to the workflow step, not discovered after the fact from the invoice.
Integrations
Orchestration of external API calls, normalization and versioning of sources, isolation from their instability. The platform guarantees that a failure or change at an external provider does not collapse the workflows that depend on it — a responsibility boundary that must not be left to each workflow separately.