There is a step in the development process for large language model (LLM)-assisted tooling that most teams skip because it's tedious, time-consuming, and doesn't produce results visible to end users: Verifying that what the model is saying is actually correct. Not fluent, not coherent, not topically relevant — correct in the sense of accurately identifying the right answer to the specific problem the tool was built to solve.The gap between "this output sounds right to me" and "this output is verifiably correct" is where most LLM-assisted enterprise tools fail quietly. They pass internal review because the output sounds right. They fail in production because those people weren't reviewing against ground truth — they were reviewing against their intuiti [...]
As enterprise AI agents take on increasingly complex, long-horizon tasks, their performance is often restricted by their harness, the software scaffolding that connects the backbone LLM to its environ [...]
Not every company can or should build their own frontier AI language model. However, the harness controlling the model is something that most enterprises can and should customize for their specific pu [...]
A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness [...]
Another day, another new AI agent harness is released.Only this time, it's one that aims to solve a growing enterprise problem as AI agents proliferate: enabling greater developer control of agen [...]
Enterprise AI is facing an ROI paradox. While throwing more compute at the strongest foundation model works well in product experiments, the costs become unbearable when the product is deployed in pro [...]
DeepSeek is expanding beyond the model layer and deeper into the software developers use to put AI agents to work.The Chinese AI lab on Thursday launched the official version of DeepSeek-V4-Pro, an up [...]
Most teams building retrieval augmented generation (RAG) systems for high stakes classification make the same architectural bet: Route every ambiguous case straight to the language model and trust the [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
Visa's open-source security harness now finds the vulnerability, writes the fix, and turns an adversarial panel on its own patch before any human reviews it. The whole loop ships on by default. A [...]