Agent Labs Are Eating the Software World (www.nibzard.com)

AI startups that are winning aren’t building bigger foundation models — they’re shipping agentic products that turn existing LLMs into goal-directed systems that actually complete work. The piece argues a new split: “model labs” focus on multi-year R&D and next‑gen models, while “agent labs” ship fast, own the full workflow (file changes, tool calls, tests, approvals), and monetize outcomes — shipped features, resolved tickets, passed tests — not tokens. Examples like Cursor, Devin/Cognition and Factory AI illustrate how product-first teams iterate from API consumers to narrow, domain-tuned models, capturing proprietary traces and feedback loops that become defensible moats and immediate revenue sources. Technically, successful agent labs converge on a stack of reasoning (planning/reflection), long-term memory, tool execution (APIs, code, DBs), and control loops (self-eval, retry), plus context engineering, multi-agent orchestration and observability. They invest more in evaluation and guardrails than raw model upgrades, optimizing task success rates, hallucination counts, cost per successful task and latency. The practical playbook: orchestrate frontier models, capture trace data, train narrow routers/embeddings, fine-tune on real signals, then build proprietary models. For founders, devs and investors this means the highest leverage shifts from model innovation to system design, evaluation engineering and workflow data — heralding a “decade of agents” where reliability and integration, not just scale, drive value.