Define Capacity as a Security Requirement
Start with written acceptance criteria instead of treating capacity as a request-count problem. Specify which requests may reach the AI layer, the maximum context that each workflow may provide, which tools are allowed, and what must happen when the application is busy. Context size, tool results, retries, and human review can all change consumption without changing the visible request count. For a hypothetical internal ticket assistant, classify short tickets, long tickets, and tickets containing retrieved records. For each class, define an acceptable queue behavior, a maximum retry policy, a redaction rule, and a clear boundary around credentials. This makes capacity planning testable without assuming any particular model behavior.
Build an Estimate from Your Own Records
Use representative, non-sensitive samples and run your own checks before changing production limits. Record input and output sizes, retry causes, tool-call counts, queue time, rejection reasons, and changes visible in NexoToken usage history. Account platform credits may help you reconcile consumption, but a historical pattern is not a guarantee of future demand. If a hypothetical workflow consumes credits faster during a busy period, check for duplicate submissions, timeout retries, oversized retrieved documents, and unbounded conversation history before requesting more access. Keep the sample definition, collection period, and actual outcomes in an engineering record so another reviewer can reproduce the calculation.
Isolate Credentials and Authorize Tools
Keep service credentials on the application server and exclude them from browser state, prompts, user-visible errors, and ordinary logs. Give each tool a narrow contract: permitted operations, accepted fields, resource scope, identity context, timeout, and refusal conditions. Validate tool arguments before execution, redact sensitive values from returned data, and reject unexpected response shapes. Treat retrieved pages, uploaded files, and user text as untrusted data even when they contain instructions that look authoritative. In a hypothetical support workflow, a document may ask the assistant to disclose an environment variable or call an unrelated administrative action. The application should block that request, return a bounded explanation, and record the policy decision without exposing the secret.
Set Thresholds and Failure Procedures
Define separate thresholds for context size, concurrent work, retry volume, tool calls, and available account capacity. For every threshold, document the owner, the signal to inspect, the queue or degradation behavior, and the condition for restoring normal processing. A safe fallback may route the task to a human, remove optional retrieval, or ask for a smaller input, but it must retain authorization and redaction checks. If usage history shows an unexpected increase, first stop suspicious retry loops and high-risk tool paths, then inspect request correlation identifiers and application logs. Use the current main platform documentation to verify NexoToken account status, available model access, credit rules, and usage-history semantics. Record actual findings, rejected assumptions, and the next review date; do not present an unperformed check as evidence of capacity or security.
Sources & further reading
NexoToken platform and community
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