Google Scientist on Long-Horizon Agents: Balancing Context vs. Scaffolding
AnneliesGamble · x · 2026-08-26
Google DeepMind Distinguished Scientist Prateek Jain discusses two high-level strategies for enabling AI agents to tackle complex, long-horizon tasks: expanding the context window to hold more of the problem in mind, or building external scaffolding (retrieval, memory, planning, tools, sub-agents) to reduce the burden on the model. The interview explores finding the right balance between these approaches.
Related event: DeepMind Scientist on Long-Horizon Agents: Context vs Scaffolding(2 posts)→
More from coding & agent
- Musk amplifies dev's verdict: Grok Bot easier to use than Codex and Claude — elonmusk · 2026-10-15
- Blob0 launches: a macOS file-manager-style desktop client for S3 and Cloudflare R2 — yihui_indie · 2026-10-15
- The 6-Layer AI Engineer Skill Stack for 2026: Vectors to Multi-Agent Ops — mdancho84 · 2026-10-15
- Building a Mechanical Engineering Agent Workflow in ChatGPT: 30-Node Canvas Shows Promise — burhop · 2026-10-15
- Open-source Harness Engineering course hits 19k stars, shows $9 vs $200 gap between failing and working agent setups — thisguyknowsai · 2026-10-15
- A 6-person team shipped healthcare prior-auth AI agents in 3 months — here's the playbook — alex_verem · 2026-10-15