I'm Ryan Lynn, founder of IntelligentNoise — the AI platform for revenue teams. Sharing some perspectives on building production AI agents, real-world implementations, and my thoughts on what's next.
Baseten's research team and Harvey are circling the same question: what should an agent keep when it cannot possibly keep everything?
Read itNico Bustamante just published the deepest practitioner guide yet on how three production agents actually implement memory—and the lessons translate beyond the CLI into how any team building enterprise agents should be thinking.
Harrison Chase, CEO of LangChain, breaks down the modern AI agent stack—why the harness matters more than the model, and the core primitives that make agents work.
OpenAI just launched Frontier, its enterprise agent platform. The most significant part isn't the agents—it's the Business Context layer underneath them, and what it signals about the future of enterprise AI.
Everyone can call an LLM API. What separates agents that work from agents that fail is context—clean, normalized, searchable knowledge that turns a generic model into a domain expert.
With 95% of AI projects failing and $67.4B lost to hallucinations in 2024, pre-deployment evaluations are the critical differentiator between success and expensive failure.