Cost Intelligence
Token spend observability across IDEs, background agents, and orchestrated workflows—with attribution you can act on
Exemplar
How this harness capability fits the Exemplar platform—governed agent operations, not a standalone prompt playground.
Why Exemplar
Agent loops can rack up surprise bills in minutes; finance sees a lump LLM invoice while engineering cannot tie cost to workflows.
Exemplar tracks token economics at the harness layer—where tool loops, retries, and orchestration steps actually happen.
What Exemplar delivers
Centralized spend dashboards across MCP sessions, orchestration runs, and gateway-routed LLM calls.
Per-workflow attribution so teams see which automations pay off and which need compaction or skill redesign.
How teams use it
Review spend by service tier, agent profile, and integration; drill into sessions that breached soft budgets.
Pair insights with guardrails—rate limits, circuit breakers, and hard budgets—to stop runaway loops before they scale.
Capability checklist
Developer guide
Official documentation on docs.exemplar.dev for this capability.
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From the blog
Related posts on exemplar.dev.
- Agent loops, tokenomics, and the harness
Why the model is no longer the product: the loop turns intelligence into work, the harness governs it, and tokenomics (token value per watt per user) decides whether it pays. Field examples from Perplexity CEO Aravind Srinivas on 20VC.
- Best Tools to Cut AI Agent & LLM Token Costs in 2026
The best tools to reduce AI agent and LLM token costs in production — prompt caching, model routing, budget enforcement, and circuit breakers. Compared and ranked for engineering teams.
- The Complete Guide to Cutting AI Agent Token Costs
Eight proven techniques for reducing LLM API token costs in production AI agents without sacrificing capability: progressive disclosure, skills, prompt caching, context compaction, model routing, batching, lean tool design, and token budgets.
- AI Agent Token Costs: 25 Questions Answered
Why AI agents cost more than chatbots, how to measure token consumption, which reduction techniques actually work — prompt caching, progressive disclosure, model routing, batching, token budgets — answered directly.