Scaling from a clever LLM demo to a reliable, long‑running AI agent in production is less about squeezing a few more percentage points out of the underlying model and more about how you “harness” that model. That’s the core argument… Read More »Why Better LLMs Aren’t Enough: LangChain’s Harrison Chase on Harness Engineering and Deep Agents
As large language model (LLM) agents move from demos into labs and production systems, the tooling ecosystem has split along two paths: sprawling orchestration frameworks on one side, and provider-locked SDKs on the other. For researchers who care about reproducibility… Read More »Orchestral AI: A Synchronous, Provider-Agnostic Alternative to LangChain for Reproducible LLM Agents
Multi-agent AI is having a moment in the enterprise. From AI “teams” of coding assistants to fleets of specialized financial agents, the implicit promise is that more agents, more tools, and more collaboration will reliably deliver better outcomes. New research… Read More »When Multi-Agent AI Helps — and When It Hurts: Lessons from New Google–MIT Research