+ P Act for Semantic Kernel agents
Capture every Semantic Kernel plugin invocation and prompt render through the Kernel's own Filters — the same interception point Semantic Kernel uses for caching and responsible-AI checks.
Prefactor acts on your Semantic Kernel 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 Semantic Kernel
Same SDK, no extra package
The prefactor-core client you already initialized for tracing is what enforcement runs through.
Wrap the action in a span
withSpan() wraps the operation you want governed — a tool call, a data write, anything your own code defines.
Policy runs before your code does
Prefactor evaluates policy inside withSpan before calling the function you wrapped — block, throttle, or hold for approval, before the action executes.
# pip install prefactor-core
import os
from prefactor_core import PrefactorCoreClient, PrefactorCoreConfig
from prefactor_http import HttpClientConfig
config = PrefactorCoreConfig(
http_config=HttpClientConfig(
api_url="https://app.prefactorai.com",
api_token=os.environ["PREFACTOR_API_TOKEN"],
)
)
client = PrefactorCoreClient(config)
await client.initialize()
# then instrument your Semantic Kernel run with spans — see docs.prefactor.aiShown with the Prefactor SDK — a first-class, working integration today.
How the Semantic Kernel integration actually works
- Semantic Kernel's Filters (IFunctionInvocationFilter, IPromptRenderFilter) run before and after every function invocation and prompt render, with access to the function, its arguments, and its result — this is the real interception point Prefactor spans attach to, the same one Semantic Kernel's own docs use for caching and responsible-AI scenarios.
- Because a function invocation filter can override the result, not just observe it, the same Filter interface is what a runtime policy check runs through — hold, replace, or block a plugin's result before the Kernel returns it.
- Beyond auto-captured spans, use withSpan to record any custom step you define — an API call, a quality check, a business action.
What Prefactor captures for Semantic Kernel 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 Semantic Kernel 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 | — | — | ✓ |
Semantic Kernel act — FAQ
How do runtime policies work for Semantic Kernel agents?+
A policy engine sits at the point a Semantic Kernel 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 Semantic Kernel 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 Semantic Kernel 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 Semantic Kernel?+
When a policy flags a Semantic Kernel 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 Semantic Kernel 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 Semantic Kernel runs.
Do I need a dedicated package for Semantic Kernel?+
You can instrument Semantic Kernel today with the framework-agnostic prefactor-core SDK; a dedicated package can be added on request.
What does Prefactor capture from Semantic Kernel?+
Prefactor records plugin (skill) invocations, planner steps, tool calls and LLM calls as structured, timestamped spans — so every Semantic Kernel run is captured as trace data you can reconstruct, search and export end to end.
Does Prefactor add latency or change how Semantic Kernel runs?+
No. Observability capture is designed to stay off your agent's critical path, so it doesn't alter your Semantic Kernel 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 Semantic Kernel 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 Semantic Kernel agents.
Keep going
Act for other frameworks
LangChain →CrewAI →LangGraph →OpenAI Agents SDK →Claude Agent SDK →Microsoft AutoGen →See it on your Semantic Kernel agents
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