When we started Agent Ai, we believed every revenue team would eventually run on agents. Two years later, we're operating that infrastructure for over 500 teams — and we've learned a lot along the way.
The challenge
Most agent platforms assume a single tenant, low concurrency, and predictable workloads. None of those assumptions hold in production.
Our architecture
We split the platform into three layers:
- Data layer — unified identity resolution across every source
- Tools layer — shared workflows, routing, and scheduling primitives
- Agent layer — LLM orchestration with full observability
Every agent action is logged, reviewed, and reversible before it touches your systems.
Why this matters
The result is a platform that handles billions of decisions per month while staying explainable and safe.
// Example: deploying an agent
const agent = await default.agents.create({
name: "Lead Router",
tools: ["enrichment", "routing", "scheduling"],
guardrails: { requireApproval: true },
});