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Prefactor
Catch your agents' mistakes before they reach customers
1.5K followers
Catch your agents' mistakes before they reach customers
1.5K 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
Drift is the one nobody talks about until it's already cost them a bad week. Real detection here feels like the right instinct.
Prefactor
@ado_audu Absolutely does. When a model can change underneath you without a notification, how can you possibly contain drift. Being able to sit inside the agent is absolute gold dust.
Prefactor
@ado_audu this guy gets it! How are you monitoring this drift in production currently, and whats preventing you from adopting something like Prefactor?
Prefactor
@ado_audu Thanks Michael! Drift is the one that hurts because there is no incident, no alert, no obvious moment where it broke.
Just a slow slide that someone notices a month later in a customer complaint.
Have you experienced this too?
The line "most agents pass their evals and fail in production" is going to resonate with more teams than you probably realize. I've said some version of that sentence out loud in at least three postmortems this year.
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@billy_boy Hey Billy, thanks for comment. You're right. I find that most companies don't even realise the difference.
I can tell someone what their evaluation approach is before they've even opened their mouth because they are so standardised. Sampled runs, LLM as judge and golden dataset. All are point in time.
They have their value in dev for sure. But in prod, you need a suite of tools which reduce the need for tokens and non-deterministic outcomes. Being in the agent run/ live, allows us to do that.
Would love to get your feedback on the tool and learn if it would have solved some of those PMs earlier in the year
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@billy_boy It's such a big problem to solve -- so many projects failing, and it really doesn't have to be that way. We're trying to put an end to those post-mortems!
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@billy_boy Three postmortems is a rough year.
That sentence came out of the same place for us too, watching teams ship something that scored well on a fixed eval set and then quietly degrade over weeks with nobody able to prove it.
What was the failure mode in those postmortems btw, was it drift or something breaking outright?
Prefactor
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.
Prefactor
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!
Prefactor
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.
Prefactor
@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!
Prefactor
@ari_shin Yeah, it shouldn't be a reporting question -- it's part of agent operations.
Prefactor
@ari_shin Yes exactly! Are you building an agent at the moment yourself?
Prefactor
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?