Manifesto | Sail Research
sailresearch.com · 475 words · saved by 1 readers
Inference + sandboxes for long-horizon agents
The inference behind every AI workload makes a trade-off between latency and throughput. In the first iteration of generative AI, systems optimized for low latency return tokens and output as quickly as possible to a user waiting on the other end. But speed comes at a cost. A large and growing share of AI work isn’t waiting on a human at all. Asynchronous use cases—like deep research, code review, security review, evals, and embeddings—require agentic pipelines that spend hours running in the background, without humans in the loop. In this paradigm, shaving milliseconds off a single…
saved by
related reading
- Sail Researchsailresearch.com
- Together AI | The AI Native Cloudtogether.ai
- My picture of the present in AI — LessWronglesswrong.com
- Building Effective AI Agents \ Anthropicanthropic.com
- AI Model & API Providers Analysis | Artificial Analysisartificialanalysis.ai
- AddyOsmani.com - Long-running Agentsaddyosmani.com
- Low Latency and Model Training at Modalrhea24.github.io
- Generative AI's Act o1: The Reasoning Era Begins | Sequoia Capitalsequoiacap.com
- Hidden Technical Debt of AI Systems: Agent Runtimeleehanchung.github.io
- AI Is Slowing Downwheresyoured.at
- Effective harnesses for long-running agents \ Anthropicanthropic.com
- Agents Over Bubbles – Stratechery by Ben Thompsonstratechery.com