+ P Act for Vercel AI SDK agents
Capture every Vercel AI SDK run through its own telemetry integration interface — the same lifecycle hooks the SDK uses for its own instrumentation, not a bolted-on tracer.
Prefactor acts on your Vercel AI SDK agents at runtime — block, throttle, sandbox, or escalate a tool call or data access before it executes, stop an agent instantly with the kill switch, and delete or redact tagged PII the moment it's encountered.
How to add act to Vercel AI SDK
Same middleware, no extra package
The @prefactor/ai middleware you already installed for observability is what enforcement runs through — nothing separate to add.
Define a runtime policy
Write a policy — block, throttle, sandbox, or escalate — scoped to an action type, a data tag, or a risk level.
Evaluated before the action runs
Every intercepted Vercel AI SDK call is evaluated against your policy before it's allowed to proceed — not flagged after the fact.
// npm install @prefactor/ai
import { init } from "@prefactor/ai";
const prefactor = init({
transportType: "http",
httpConfig: {
apiUrl: process.env.PREFACTOR_API_URL!,
apiToken: process.env.PREFACTOR_API_TOKEN!,
agentIdentifier: "1.0.0",
},
});How the Vercel AI SDK integration actually works
- Registers as a Telemetry integration — via registerTelemetry() globally, or passed per-call through the telemetry option (the current name for what generateText/streamText still accept as the deprecated experimental_telemetry alias).
- The SDK's own executeTool and executeLanguageModelCall wrappers are the real enforcement point: each wrapper receives the actual tool or model call as a function before it runs, and decides whether to call it, throttle it, or block it — the same mechanism the SDK uses internally to compose multiple integrations, not a side channel.
- Beyond the auto-captured lifecycle events, use withSpan to record any custom step you define — an API call, a quality check, a business action.
What Prefactor captures for Vercel AI SDK agents
Runtime policy enforcement
Every tool call, API request, or data access is evaluated against your rules at the point the agent tries to act — block, throttle, sandbox, or escalate, in sub-millisecond time.
Kill switch
Stop a single run, an agent, or every agent a team owns — immediately, no code deploy, triggered natively from Prefactor or programmatically from a custom span.
Human-in-the-loop approval
A flagged action pauses execution and routes to the right approver with full context — what the agent was trying to do, why, and its recent history — through Slack, Teams, or email.
PII deletion
Find every field carrying a tag and remove it in one action, wherever it appears — plus real-time redact, block, or escalate the moment PII is first encountered.
Trace anything — not just LLM calls
The SDK captures LLM, tool, and agent spans automatically. With withSpan you wrap any operation in your own span type — an API call, a database query, a quality check, a business action — each with its own payload and schema. It all flows into the same Observe, Evaluate, and cost views.
import { withSpan } from "@prefactor/core";
// Wrap ANY operation in a span you define — an API call, a quality
// check, a business action — with its own spanType, inputs and schema
await withSpan(
{
name: "research competitor",
spanType: "research_competitor",
inputs: { competitor },
},
async () => {
const results = await search(competitor); // your tool / API calls
return summarize(results); // captured as one span
},
);Nest spans to capture business-level actions, and start with permissive schemas you tighten over time. Instrumentation strategy →
An example run, span by span
Illustrative — a single Vercel AI SDK run as nested spans.
Illustrative example.
Manual logging vs DIY vs Prefactor
| Capability | Manual | DIY OpenTelemetry | Prefactor |
|---|---|---|---|
| LLM / tool / agent spans | Hand-rolled | ✓ build it | ✓ via the SDK |
| Token usage captured per call | — | Build it | ✓ |
| Configurable capture & sampling | — | Partial | ✓ |
| Hosted Admin UI (agents, instances) | — | — | ✓ |
| Risk profiles & audit trail | — | — | ✓ |
Vercel AI SDK act — FAQ
How do runtime policies work for Vercel AI SDK agents?+
A policy engine sits at the point a Vercel AI SDK agent calls a tool, queries an API, or touches data — the same interception point Prefactor uses across every framework. Every attempted action is evaluated against the agent's identity, permissions, and current context before it's allowed to proceed.
Does enforcement add latency to Vercel AI SDK runs?+
No meaningfully — policy evaluation runs in sub-millisecond time, so enforcement doesn't add noticeable latency to the agent's response.
Can I kill a single Vercel AI SDK run without stopping the whole agent?+
Yes — the kill switch scopes to a single run, a single agent, or every agent a team owns. Trigger it from the Prefactor dashboard directly, or programmatically via a custom span the moment your own code detects a problem.
How does human-in-the-loop approval work for Vercel AI SDK?+
When a policy flags a Vercel AI SDK agent's action for escalation, execution pauses and an approval request goes out with full context. The approver's decision is logged and either lets the action proceed or cancels it.
How is PII actually deleted from Vercel AI SDK agent data?+
Detection and tagging happen continuously; deletion is one action against the tag — find every field carrying it for a given person or record, and remove it, wherever it appears across your Vercel AI SDK runs.
Do I need a dedicated package for Vercel AI SDK?+
Vercel AI SDK has a native package (@prefactor/ai) that registers as a Telemetry integration; you can also instrument it today with the framework-agnostic prefactor-core SDK.
What does Prefactor capture from Vercel AI SDK?+
Prefactor hooks the SDK's own telemetry lifecycle — onStart/onEnd, onStepStart/onStepEnd, onLanguageModelCallStart/End, and onToolExecutionStart/End — as structured, timestamped spans, so every Vercel AI SDK run is captured as trace data you can reconstruct, search and export end to end.
Does Prefactor add latency or change how Vercel AI SDK runs?+
No. Observability capture is designed to stay off your agent's critical path, so it doesn't alter your Vercel AI SDK logic or your users' responses. The only part that acts inline is the optional runtime guardrails you enable per agent — by design, so a high-risk or low-confidence action can be held for human approval before it executes.
Can I evaluate agents built with Vercel AI SDK and catch regressions?+
Yes. Once runs are captured, eval suites score quality and groundedness on real traffic, drift detection flags behaviour changes after deployment, and versioned eval history catches regressions before they ship — the observe → evaluate → improve loop applied to your Vercel AI SDK agents.
Keep going
Act for other frameworks
LangChain →CrewAI →LangGraph →OpenAI Agents SDK →Claude Agent SDK →Microsoft AutoGen →See it on your Vercel AI SDK agents
Book a 15-minute setup and our team gets you act in production.