Prefactor
Catch your agents' mistakes before they reach customers
1.4K followers
Catch your agents' mistakes before they reach customers
1.4K followers
We evaluate every step your agents take, live. Errors get caught as they happen.
Not in a Slack thread at 1am on a Tuesday
Hi folks, I'm Simon - CTO and other co-founder of Prefactor. The real challenge for companies now is not building agents, but managing the infrastructure and process around them. A lot of the lessons learnt from deploying traditional software at scale still apply, but agents pose new and unfamiliar challenges. It's a combination of traditional devops and HR. The approaches to risk and quality that have worked in the past need to be updated.
Prefactor makes this approachable for any engineering team. Closing the loop on turning feedback into improvements to the agent; ensuring consistency over model and prompt updates; simple ways to understand and contain risk; ways to monitor and control the actions of agents across frameworks and deployment environments.
Hey guys Ethan here, I'm part of the team at Prefactor.
My main focus is on the go-to-market side which means I pretty much spend my week on calls with teams and engineers running agents in production.
I usually hear the same stuff all the time.
For example: The agent worked in testing and POC, it went live, but now nobody can answer a basic question: is it doing what we told it to?
That’s where we’d come in, Prefactor gives those teams visibility into what their agents are actually doing in production, so accountability doesn't stop the moment it's in production.
If you've got an agent running live right now, what does your monitoring actually look like? Keen to know if anyone here has actually solved this properly and if so how.
Cheers!
Hi everyone, Joseph here. I’m a designer at Prefactor.
Something I’m particularly interested in that’s easy to overlook: the moment Prefactor flags something mid-run, a human has to look at a screen and decide whether to step in or let it ride. That decision is only as good as the interface it happens on.
Agents generate an enormous amount of data, and most tools just show you all of it. My job is making sure that when something's going wrong, you can tell in seconds, not after scrolling through a wall of spans. Real-time control needs real-time legibility.
If you try Prefactor, I’d love to know: did you know where to look, or did you have to dig?
The live scoring plus action piece is the part that clicks for me. If an agent drifts in production, finding out three days later is already too late.
@ari_shin completely agree, too many instances where an agent goes off the rails only it to be discovered days later. A control loop for agents is essential infrastructure for taking agents into production. Let us know if you manage to give the platform a spin, we'd love your feedback!
@ari_shin Yeah, it shouldn't be a reporting question -- it's part of agent operations.
@ari_shin Yes exactly! Are you building an agent at the moment yourself?
Friends, Josh here. AI Engineer @ Prefactor. I built the agent instance tracking that scores quality and flags data risk while your agent runs, plus the SDKs and docs that get teams from zero to live without guessing.
Here's the thing I keep coming back to. You can't have confidence in something you can't see. An agent in production is a bucking bronco; it'll throw you the moment you stop paying attention. I've talked to too many teams who deployed, watched it work for a week, then realised they had no idea what it was actually doing. No visibility, no guardrails, just hope.
That's the piece Prefactor owns: the quality and data risk scoring that runs in-process, flagging misbehaviour mid-gallop rather than in a trace you read after the damage is done.
If you've got agents live right now, how do you actually know they're behaving? Or are you just holding on and hoping?
I spent months building internal scripts to catch exactly this kind of drift. Would've saved me a lot of late nights to just plug into something like this instead.
@malka_parveen many such cases unfortunately... Which is the reason we have built Prefactor, to save your engineering team the time and burden of building, and maintaining a platform like this. We should compare notes at some point, would love to know if we missed anything you'd consider essential for keeping your agents on the straight and narrow.
@malka_parveen Thanks Malka! Would love for you to try it out and get your feedback!
What sort of agents have you built?
How does Prefactor decide what parts of the code need attention without creating unnecessary changes? Congrats @ethan_lee8 & team!
@ethan_lee8 @hamza_afzal_butt We dont claim to know every agent and how they work. We give you the tools to track things as they go wrong, whether thats through meta data, llm as judge evals etc.
We help the unnecessary change bit by version controlling each agent, enabling different environments so you can track the problems as they occur in dev and when you're ready push them to prod.
@hamza_afzal_butt Hey Hamza thanks so much for the support!
Slight clarification, we don't touch your code. Prefactor sits at runtime and records what the agent actually does on every run, then optional guardrails can hold a high-risk action before it executes.
So it's less "which code needs changing" and more "which agent stopped doing its job, and stop it now."
Are you running any agents in production at the moment btw?