Session memory without token blowout
Naive implementations keep the full conversation history in context for every API call. At scale this means every conversation eventually hits the context limit and fails. We implement session-scoped memory with selective compression — preserving intent and key facts while managing token consumption.
Rationale: A session that fails at message 40 because the context window is full is not a production system. Memory management is a first-class concern, not an afterthought.
Trade-off accepted: Slightly more complex memory architecture in exchange for conversations that do not fail at scale.
Human handoff with full context transfer
When the model reaches its confidence boundary, it hands off to a human — with the full conversation context, the model's last reasoning state, and a suggested next action. The human agent does not start from scratch.
Rationale: A handoff that drops context is not a handoff — it is a restart. The human agent should be able to read the conversation and continue without asking the customer to repeat themselves.
Trade-off accepted: More complex handoff state management in exchange for seamless human escalation.
Cost attribution per tenant per conversation
In multi-tenant AI systems, token cost must be attributed accurately. We instrument every LLM call with tenant_id and conversation_id. Monthly cost reports per tenant are a first-class feature, not an afterthought.
Rationale: Without cost attribution, one high-volume tenant can make the entire system unprofitable without any individual transaction appearing unusual. Attribution from the first API call means cost anomalies are visible immediately.
Trade-off accepted: Additional instrumentation overhead in exchange for full cost visibility per tenant.
Graceful degradation
When the LLM API is unavailable or returns an error, the system degrades gracefully — queue and retry for async workflows, immediate human handoff for synchronous conversations.
Rationale: An AI system that fails hard when the model API is unavailable is not production-grade. Graceful degradation means the business continues to operate even when the AI layer is temporarily unavailable.
Trade-off accepted: More complex fallback logic in exchange for operational continuity under failure conditions.