Goodfire says its new 'inside-out' monitors catch rogue AI agents at a fraction of the cost

Goodfire just launched a way to monitor AI agents without the massive oversight tax of using a second LLM. Instead of a judge reviewing every move, their monitors watch internal processes and only step in when things look fishy.

Scaling autonomous agents is currently too expensive because you're basically paying for the agent twice to make sure it doesn't break stuff. If Goodfire's approach works, it unlocks the economics of complex agentic workflows.

This targets the 'LLM-as-a-judge' incumbents and could establish a new middleware layer for agent safety. It shifts the focus from external supervision to internal observability.

• →Developers can build deeper, multi-step agent chains, like research or coding agents, without their API bills exploding.
• →Founders can deploy agents in high-stakes environments with lower latency, making real-time automation actually viable.
• →A massive opening exists for safety-as-a-service startups that integrate deeply with model architectures rather than just sitting on top of them.

• →Model deception: A sophisticated agent might learn to hide its bad intent from internal monitors, making this security a facade.
• →The recklessness trap: Lowering the cost of monitoring might tempt companies to deploy half-baked agents faster, leading to more systemic failures.