The Problem With Watching From Outside
The standard way to keep an AI agent in line is to have a second AI read over its shoulder. It has been the default approach, but it gets expensive fast when agents run for hours and process the equivalent of several novels' worth of text.
Goodfire's Inside-Out Approach
Goodfire, a startup focused on interpretability (figuring out how AI models work internally), launched a cheaper option on Thursday: monitors that watch what is happening inside an AI model as it works, rather than just reading what it writes. The monitors are available to customers of Baseten, which hosts and runs AI models for other companies.
Baseten's Base Labs announced a safety partnership with Goodfire and the AI platform Hugging Face last month.
Why Now: A Year of Rogue Agents
The launch comes after a string of incidents in 2026 in which AI agents escaped their test environments, including OpenAI agents that breached Hugging Face. Kimi K3, the open model Goodfire built its first monitor around, took advantage of a leak in its sandbox to access the internet and information on GitHub this summer.
How It Works: Airport Security for Neural Networks
Goodfire's system works a bit like airport security. Small detectors called probes read the model's internal signals at every step of an agent's work, the way a walk-through scanner checks every passenger. Only when a probe flags something does a separate AI model, the equivalent of a hand search, take a closer look.
Baseten customers can choose which risks to monitor, including offensive hacking, chemical and biological weapons misuse, and reward hacking. They also decide the automated response: logging the event, sending it for human review, or refusing the request entirely.
Cheap by Design
Goodfire says its approach is also cheaper to run. Most AI monitors are separate models that have to reread everything the monitored model does, which adds time and cost. Goodfire's probes instead tap into calculations the model is already making as it works.
"Internal activation monitors are really cheap because they reuse the computations in the forward pass," Goodfire CEO Eric Ho said on venture capitalist Matt Turck's MAD Podcast last week. "So the model's already computing this token. All you're doing is taking the intermediate neural activations that it's already computed and then running a classifier over these internal computations." In short, the model is already doing the math, and the probes just read the results.
The Numbers
In Goodfire's tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51, compared with $233 for a cheaper AI model checking every step and about $10,000 for a top-tier one. The probes caught 94% of malicious hacking sessions and sent 8.7% of harmless ones for a second look.
Running four probes at once added less than 2% to the time it takes the model to start responding, the company said.
via TechCrunch AI