From impressive demo to dependable product.
The engineering decisions between a working agent and a service customers will pay for.
OpenAI · Amazon EKS · product telemetry · AgentOps
The question
What changes when an AI prototype gets its first enterprise customer? Identity, data boundaries, reliability and commercial expectations become part of the product. They cannot all be deferred to the release after launch.
The hard decision
Make one workflow dependable before making the agent broadly autonomous. Define which actions the product can take, when a user must approve them, and what can be undone. Choose managed services or EKS based on actual runtime and operating needs.
Inside the system
A tenant-aware application controls identity, entitlements and tool access. Models are reached through a deliberate API boundary. Long-running work uses queues and durable state, with idempotent tool execution and observable transitions. Evaluation and product telemetry are connected to releases.
The failure mode worth designing first
A request can partially succeed and then time out. The product needs to know what happened, show the user an honest state and recover without duplicating a consequential action. This is as much application engineering as AI engineering.
The operating measures
Track completed user journeys, evaluation regressions, error budgets and cost per tenant or outcome. Monitor model and tool latency separately. Use support findings to improve both the product and its evaluation set.
What the client owns
The application, infrastructure definitions, deployment pipeline, evaluation suite and operating playbook. The team should be able to ship, explain and recover its product with confidence.
What would this look like
in your business?
A working conversation about the opportunity, the constraints and a useful first step.