One portfolio for every agent your teams shipped
Agents spread across teams and frameworks faster than any one person can track by asking around.
Prefactor is an agent evaluation platform for teams building AI agents: it observes every agent run from development to production, evaluates whether the agent did its job, and shows how to improve it.
Works across LangChain, CrewAI, AutoGen, Claude Agent SDK, and custom frameworks: one portfolio, not one tool per team.
How do I see how all our AI agents are performing across teams?
Put every agent in one portfolio view. Prefactor shows each agent's owner, quality trend, cost per agent and version, whatever framework the team built it on, including LangChain, CrewAI, AutoGen, the Claude Agent SDK and custom stacks. Because every run is evaluated, the answer to whether an agent is working comes from evidence rather than from asking each team.
Every team can tell you about their own agent. No one can tell you about all of them.
AI adoption rarely rolls out from one place. It shows up team by team, framework by framework, each one reasonable on its own.
Four teams, four frameworks, and no view across all of them: total exposure, total spend, and which agents are quietly drifting from what they were built to do.
No single list of what's running
Agents get deployed by whichever team needed one, on whatever framework they already knew. Six months in, nobody has a complete list of what's live, who owns it, or what it can access.
Small changes, compounding risk
Permissions, prompts, and integrations change continuously. Any one change looks harmless in isolation; the risk shows up only once you can see it accumulate across a run history.
Spend nobody can explain
AI spend shows up on the bill before anyone can say which agent, team, or task drove it, so a cost conversation starts with an invoice instead of a number you already had.
What you get on day one
Every agent, one inventory
Owner, framework, version, and environment for every agent, registered automatically from a CI/CD deploy step or manually for anything ad hoc. See the agent registry → for how registration works.
Spend, attributed to a task
Every model call and tool invocation is tagged with the agent, team, and task that spent it, so a cost spike traces back to a run instead of a line on next month's invoice. See cost tracking →.
A record that holds up to scrutiny
Every action, policy decision, and approval is written to a tamper-evident trail, searchable and exportable when a regulator, auditor, or your own security team asks how an agent behaved. See the audit trail →.
Low risk bar, ships fast, minimal eval.
Independent, regulator-driven audit.
A banking team runs both side by side in one platform, each with its own risk profile, so a risk trend on the internal side gets caught before it crosses a threshold, and a single risky run gets stopped, natively or via a custom span, without touching the rest of that agent's traffic. See the full case study →.
Knowing which agents need attention, and acting on it
A portfolio view only helps if it tells you where to look first, and gives you a way to act once you do.
Every run gets classified, every policy runs at the point an agent tries to act, and a high-risk action reaches the right person instead of a log nobody reads.
Risk scored on two axes
Data sensitivity and action consequence combine into a Low / Medium / High / Critical classification per run, so a review starts with the runs that actually warrant it. See risk monitoring →.
Rules that run at the point of action
Block, throttle, sandbox, or escalate a tool call or data access as it happens. The same rule applies whether the agent runs on LangChain, CrewAI, or something a team built in-house. See runtime policies →.
A human in the loop, with context
A flagged action pauses and routes to the right approver with full context on what triggered it, through Slack, Teams, or email, with no need to chase down the team that owns the agent first. See approval routing →.
A governance framework tells every team what the rules are supposed to be. It has no way to check whether an agent is actually following them at 2am on a Saturday. Prefactor doesn't replace the policy document: it's the layer that enforces it at runtime, with a record of every time a rule held or didn't. See Prefactor vs. governance documentation platforms → for the honest breakdown of where each one fits.
The problems this solves
Frequently asked questions
How does Prefactor give me visibility across every team's agents?
Can Prefactor measure agent quality across different teams and use cases?
How does Prefactor help manage AI costs across the organization?
Does this work if my teams use different agent frameworks?
What happens when an agent crosses a risk threshold?
See your own portfolio, not a demo fleet
Book a demo and we'll walk through what a portfolio view looks like across teams and frameworks like yours.
Unified performance platform for agents, authentication, and risk management