#llm-agents

#llm-agents

In an effort to probe the limits of autonomous software development Anthropic researcher Nicholas Carlini used sixteen Claude Opus 4.6 AI agents to build a Rust-based C compiler from scratch. Working in parallel on a shared repository, the agents coordinated their changes and ultimately produced a compiler capable of building the Linux 6.9 kernel across x86, ARM, and RISC-V, as well as many other open-source projects. The agents ran roughly 2,000 sessions without human intervention, incurring about $20,000 in API costs.

During my eight years working in agile product development, I have watched sprints move quickly while real understanding of user problems lagged. Backlogs fill with paraphrased feedback. Interview notes sit in shared folders collecting dust. Teams make decisions based on partial memories of what users actually said. Even when the code is clean, those habits slow delivery and make it harder to build software that genuinely helps people.

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Take the example of workflow automation software company Pegasystems, which recently celebrated a financial turnaround on the back of its switch from process automation to so-called agentic AI, causing third-quarter revenue to rise 17 percent from the previous year. "In applications, Pegasystems is working with us to take legacy applications which may have run on-prem or in a hybrid environment and modernize them in the cloud," he tells The Register. The vision is that legacy applications can be combined with newer elements and sewn together with LLM agents to present the end user with a coherent experience. Although AWS doesn't provide applications or LLMs, it is building infrastructure and tooling to support this. Under the hood, Pegasystems uses AWS Bedrock, a managed service offering a choice of foundational models through a single API.

In a rapidly evolving tech landscape, establishing structured feedback loops for AI products ensures that real user signals continuously refine and enhance model performance.