Your AI agents are spending money. Are they worth it?
Layeroi = CFO-readable P&L per agent.
Built for the people holding the budget, not the ones writing promptlayeroi.com GlobalJoined April 2026
The pattern repeats every 10 years in enterprise software.
2010: Cloud was a dev tool.
2015: Cloud became a CFO line item.
2018: SaaS was a dev tool.
2023: SaaS became a CFO line item (hello, Vendr & Zylo).
2024: Agents are a dev tool.
2026: Agents will become a CFO line item.
layeroi.com
Every AI infra company is optimizing the wrong metric.
• LangSmith: traces
• Helicone: logs
• Langfuse: prompts
Nobody is showing CFOs the P&L.
That's not a product gap. That's a category gap.
layeroi.com
The quiet truth about agentic AI:
80% of agent calls add zero business value.
15% add marginal value.
5% do all the work.
And nobody knows which is which —
because the tooling is built for engineers, not for the people holding the budget.
layeroi-sdk v0.2.0 ships with Anthropic support.
3 lines of code, every Claude call attributed:
import { layeroi } from 'layeroi-sdk'
const claude = layeroi.wrap(new Anthropic(), { agent: 'support' })
OpenAI + Anthropic. Real-time cost per agent. Your CFO will love you.
npm install layeroi-sdk
@datadoghq has 27,000 customers watching their LLM traces.
Not one of them can tell you which agent is profitable.
Traces ≠ P&L.
Tokens ≠ ROI.
There's a $10B gap between "we have observability" and "we have economics
Talked to a company yesterday running 4,200 agent calls/day on GPT-4.
Monthly cost: $18,400.
Business value produced: ~$2,100.
They thought they were "scaling AI." They were scaling a leak.
If you can't answer "cost per successful agent outcome," you're flying blind.
Layeroi.com
Your company is running 1,000s of AI agent calls every day. Your CFO has no idea which ones make money. Your engineers have traces. Your finance team has nothing. Layeroi closes that gap.
CFO-readable P&L per agent. Live dashboard in 15 minutes. → layeroi.com
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