Define the workload envelope
Begin with the work the platform must support. Describe task categories, input sizes, expected response sizes, arrival patterns, simultaneous work, and the required response times. Identify which activities can wait and which have an operating deadline. Preserve representative traces or approved workload descriptions with their dates and collection limits. The first worksheet should make the expected range visible, including quieter periods and demanding combinations. This gives the capacity discussion a common basis before anyone selects a hardware configuration or extrapolates a fleet requirement.
Make context policy an explicit design input
Document what information each task receives, how that information is selected, and when it expires. Separate current instructions, retrieved evidence, conversation history, and retained working information. Record the model and runtime configuration alongside the context policy, then measure their resource use on the agreed tasks. A maximum advertised context window does not establish the capacity of the complete workflow. Review how retrieval, compaction, and retention choices affect accepted results, and preserve the evidence needed to revisit a choice that changes task quality.
Decide what state must survive interruption
List the information required to continue or safely resolve interrupted work: task identity, source revisions, completed steps, pending requests, approvals, and the last confirmed business outcome. Assign an authoritative owner and retention rule to each item. Then describe recovery at meaningful boundaries, such as an interrupted retrieval or an action whose receiving system has not confirmed its result. A capacity plan should account for the resources and time required by that recovery path. It should also establish when a person must resolve uncertainty before work continues.
Connect application sizing with facility constraints
Keep compute, memory, storage, network, and fleet assumptions in a shared register. State the expected utilization and redundancy assumptions used in each scenario. Bring the resulting requirements to the people responsible for power, cooling, connectivity, and operational capacity, who can assess them against the particular facility. Preserve the distinction between a calculated resource requirement and a measured configuration. A planning model can organize questions and expose sensitivities; a claim that a workload fits a particular installation requires evidence from that installation and its operating conditions.
Test busy and interrupted paths
An approved evaluation should include overlapping tasks, longer context, backlogs, and recovery as well as ordinary completion. Define the test boundaries with the operating team and retain configuration, input, resource, and acceptance records. Observe what happens to response time, accepted-task rate, retained state, and recovery correctness as the workload changes. Include the resource cost of retries and verification. Record the point at which an operating requirement is missed, rather than smoothing it into an average that makes a constrained system appear ready for every expected condition.
Use the evidence to choose the next scope
Summarize each option with its workload assumptions, measured behavior, modeled bounds, facility dependencies, and unresolved questions. Identify the reviewer responsible for accepting each conclusion. AssetSolve AI Inference Economics connects capacity and state sizing, context-policy analysis, fleet and power scenarios, and evaluation gates in this decision process. A bounded engagement can begin with one workload and its quality floor, then produce a measured baseline and an architecture decision record. Broader capacity commitments should follow the evidence gathered for the intended operating envelope.