Instead of using one large model for all tasks, Linear employs a small router model. For high-frequency use cases like creating issues, it routes the request to a smaller, highly-optimized model and prompt, saving costs while improving performance and reliability.

Don't use your most powerful and expensive AI model for every task. A crucial skill is model triage: using cheaper models for simple, routine tasks like monitoring and scheduling, while saving premium models for complex reasoning, judgment, and creative work.

Moving from simple chatbots to autonomous agents creates a massive cost increase. Agents consume 5 to 30 times more tokens because they operate in loops, with each task involving 10-20 separate model calls that carry extensive history, instructions, and tool definitions, rapidly compounding costs.

Don't use the most powerful and expensive AI model for every task. Use cheaper, faster models like Anthropic's Haiku for high-volume, simple jobs and reserve powerful models like Opus for complex reasoning. This strategy can reduce costs by over 99%, turning a potential $150 task into a $1.50 one.

An effective cost-saving strategy for agentic workflows is to use a powerful model like Claude Opus to perform a complex task once and generate a detailed 'skill.' This skill can then be reliably executed by a much cheaper and faster model like Sonnet for subsequent use.

Running premium AI models constantly is prohibitively expensive. A cost-effective strategy is to use a cheaper model as a "manager" to understand a high-level goal, break it down, and then delegate the execution of sub-tasks to multiple, short-lived "child" sessions running more powerful models.

A hybrid approach to AI agent architecture is emerging. Use the most powerful, expensive cloud models like Claude for high-level reasoning and planning (the "CEO"). Then, delegate repetitive, high-volume execution tasks to cheaper, locally-run models (the "line workers").

As enterprises scale AI, the high inference costs of frontier models become prohibitive. The strategic trend is to use large models for novel tasks, then shift 90% of recurring, common workloads to specialized, cost-effective Small Language Models (SLMs). This architectural shift dramatically improves both speed and cost.

A single AI agent can run multiple "sub-bots" for different tasks. To optimize performance and cost, assign different underlying models to each. Use a powerful model like Claude Opus for complex tasks, and a cheaper model like Sonnet for routine functions.