Prefactor's real-time agent eval loop closes the 'observe but don't stop' gap

A new governance platform called Prefactor launched on Product Hunt today, and it is worth a closer look for anyone building production AI agents. The pitch is straightforward: score every agent run for quality, drift and risk the moment it happens, then wire those scores directly into enforcement actions. No dashboards that tell you an agent leaked PII after the fact. No charts that require a human to read them and flip a switch.

The company published its pitch on July 29, 2026, and the landing page wastes no time naming the problem it solves. “Observability and monitoring hand you dashboards (traces, scores, alerts) then hand the problem back,” the site reads. “By the time you’ve read the chart, the agent has already acted.” That gap between observation and intervention is the core insight, and it is one that existing observability tools have not closed.

Prefactor offers TypeScript and Python SDKs with native integrations for LangChain, Claude, Vercel AI, OpenClaw, and LiveKit. The CLI connects a workspace and discovers agents across runtimes in one command. The docs describe a five-step workflow: install, instrument, gain runtime visibility, evaluate, and enforce. Each step maps to a live feature. The platform records every run as structured trace data, scores it against user-defined evals (LLM-as-judge, technical checks, qualitative metrics, and data-risk detection across 17 sensitive-data categories), and then acts on those scores at runtime.

The action piece is what distinguishes Prefactor from the broader observability market. A failing score does not just paint a red line on a chart. Prefactor can pause a run, hold a high-risk action for human approval, block it entirely, or throttle it. The enforcement happens through the SDK or API, and the decision is logged. The company calls this the “Reliability Trifecta”: observe, evaluate, act. The third leg is the one most tools leave out.

The timing is right. AI agents are moving from prototype to production at a pace that surprises even the people building them. LangChain, Vercel AI, and Claude have made it trivial to spin up an agent that calls tools, writes to databases, and executes actions with real-world consequences. The bottleneck is no longer building the agent. It is knowing whether the agent is doing the right thing, every time, and stopping it when it is not.

Prefactor’s docs frame this as an agent development lifecycle problem. The platform versions every agent, tracks it against a schema, and promotes it through dev, staging, and prod only when its evals pass. The versioning is immutable, using semver, commit, or tag identifiers. Schema management uses a two-tier system that tracks schema and deployment versions independently, surfacing conformance drift when a run breaks its declared shape. The promotion pipeline is eval-gated, and rollback is instant.

The company claims a financial services customer with forty agents in production could not honestly say which ones were still doing their job before adopting Prefactor. The landing page cites sub-five-minute install times and runtime enforcement via SDK or API. The free tier covers 25,000 spans per month.

There are open questions. Prefactor is a young product, and the security posture page notes that SOC 2 Type II is in progress and RBAC is on the roadmap. The platform runs on a per-account, per-environment model that separates dev, staging, and prod activity, which is standard for enterprise tooling but requires users to buy into Prefactor’s abstraction layer. The integrations list is strong for the agent frameworks that matter today but will need to keep pace as new orchestration layers emerge.

The broader signal is clearer than the product details. The market for agent observability and governance is forming in real time, and the tools that win will be the ones that close the loop between evaluation and enforcement. Dashboards are not enough. Charts are not enough. Alerts that ping a Slack channel and wait for a human to read them are not enough. The agents run faster than the humans can react.

Prefactor is betting that the next layer of the AI stack is not a better model or a faster inference engine. It is a governance layer that sits between the agent and the action, scoring every step and deciding, in milliseconds, whether to let it proceed. That bet is worth watching, because if agents are going to handle real transactions, real customer data, and real decisions, the question of who or what stops them when they go wrong is not a feature request. It is a requirement.

The outstanding question is whether Prefactor can scale its enforcement model to the latency requirements of real-time agent systems without becoming the bottleneck itself. The docs claim enforcement in 42 milliseconds for one action type. That number will need to hold at thousands of concurrent runs.