Customer support agents are among the most demanding production workloads for large language models. They require long conversation histories, retrieval-augmented context, tool calls to ticketing systems, and strict latency constraints. For teams building these systems, inference costs usually scale with every token of context injected into the prompt, which makes long-context architectures expensive to run at scale. Oxlo.ai offers a different foundation: flat per-request pricing that does not increase with input length, making it particularly cost-effective for agentic support workflows that carry large prompts. Core Architecture for Support Agents A production support agent typically combines three components: retrieval to ground responses in internal documentation, memory to maintain multi-turn conversation state, and tool use to perform actions like creating tickets or looking up orders. The LLM serves as the reasoning layer that decides when to retrieve context, call a function, or respond directly.…