One of the most common questions executives ask right now sounds straightforward: is the agent reliable enough yet? It feels like the right place to start, but the framing quietly points people in the wrong direction because it assumes something about reliability that has rarely been true in complex systems. When people ask whether an agent is reliable, they are treating the agent itself as the unit of reliability, something you either trust or do not trust in the same way you would evaluate a database or an API. That mindset comes directly from how software has traditionally been built over the last few decades. Teams evaluate components in isolation, stack them together, and expect the overall system to inherit the guarantees of the underlying parts. High-stakes human work has never really operated that way, and agentic systems probably will not either. Even today, most production agents are already layered systems in practice. One model plans, another executes, and a third revi...
Ornith, a new family of open source LLM models from the DeepReinforce research collective, takes a novel approach to executing coding and debugging tasks: It generates an architectural framework to give the user’s harness a structured instruction set – a scaffold – to create an agent to complete the job. Available in a set of four variants, the Ornith family was trained to work comfortably with complex software repositories undertaking complicated long-horizon jobs. Sure, LLMs can do these tasks now – until the job gets too complex. Ornith’s self-generated scaffolding ensures that it doesn’t forget the plot along the way. “The model continuously improves not only its code generation abilities but also the orchestration strategy used to solve software engineering problems,” wrote AI tutorial engineer Mehul Gupta, in an introductory post . Deep Reinforcement Expansion Pack Ornith reads the user’s instruction, but instead of executing it directly it builds a scaffold, a learnable ob...