Temporal CEO on AI Agents & The Future of Software | Deep Dives with a16z

a16z Deep Dives · 2026-02-19 · 63 分鐘
https://www.youtube.com/watch?v=NaIiiON5Sj4影片總結
Temporal powers OpenAI Codex and Snap at scale, bringing durable state, recovery, and RPC to long-running AI agents.
Temporal CEO Samar Abbas explains how durable execution records application state, resurrects failed workflows on another host, and guarantees completion despite distributed-system chaos. The platform grew from Cadence at Uber, where it handled tipping retries lasting up to 3 days, loyalty workflows, and recovery from a bug that reset customer points. Abbas says Temporal now powers OpenAI Codex and Snap’s story processing, with its cloud handling bursts of 150,000 actions per second. As agents shift from interactive prompts to asynchronous, long-running work, they need recoverability, sandboxes, observability, context engineering, and durable RPC. Temporal’s execution histories provide auditability and analytics, while Project Nexus aims to standardize asynchronous tool calls between specialized agents. Abbas argues SaaS is not dead: value will accrue to APIs, reliable infrastructure, and breakthrough applications such as Harvey and healthcare tools.
章節
- 0:00Introduction: Durable Execution Guarantees Exactly-Once Order Processing
- 4:03Temporal’s Origin Story: Cadence Solved Uber’s Microservice and Loyalty-State Failures
- 11:14Why Agents Raise the Stakes: AI Reliability Requires Multi-Region Continuity
- 16:00Specialized Agents Need Durable RPC: Background Agents Move Beyond the MS-DOS Era
- 20:01Specialized Agents Need Durable RPC: Codex Orchestrates Long-Running Coding Work
- 25:20Deep Research Agents: Parallel, Human-in-the-Loop Workflows Need Recovery
- 30:58Execution Histories as a Superpower: 100x Scale and Auditable Agent Traces
- 35:03Execution Histories as a Superpower: APIs Preserve Value as Software Expands
- 39:04Minimal Viable Long-Running Agent Architecture: Sandboxes, Evaluation, and Systems of Record
- 45:07Context Engineering at Scale: Real-Time Retrieval and Durable RPC for Agent Swarms
- 52:40Where Value Accrues: Jensen Huang’s Five-Layer Cake and Breakout AI Applications
- 56:01Where Value Accrues: Temporal’s 2021 Lessons, Margins, and R&D Expansion
這是 Tier 1 公開摘要
每章重點、段落總結、心智圖由分享者控制是否公開。想看完整分析?自己提交一支。
同頻道的其他分析
Mintlify and the Transition From Human Docs to Agent Infrastructurea16z Deep DivesMintlify’s Han Wang and Hahnbee Lee explain eight pivots, a two-day prototype, and docs becoming AI infrastructure.
To Regulate AI Effectively, Focus on How It’s Useda16z Deep DivesMartin Casado argues AI laws should target illegal use, while US regulatory uncertainty pushes startups toward Chinese open-source models.
How Palantir Scaled: Why the Best Software Is Built Backwardsa16z Deep DivesPalantir’s Akshay Krishnaswamy explains FDEs, backward product building, Foundry, and avoiding the consultancy trap.
Inferact: Building the Infrastructure That Runs Modern AIa16z Deep DivesInferact founders explain vLLM’s rise, 500,000-GPU scale, and a universal open-source inference layer
AI Copilots Are a Dead End. Here's What Actually Works | Kavak CEOa16z Deep DivesKavak uses AI agents for 90–95% of customer interactions after a flat 2023, then grows four times.
相關主題的分析
股市暴跌,無人消費:AI贏了,但白領消失了?!《全球智能危機》Better Leaf 好葉《2028年全球智能危機》警告AI裁員、13兆美元房貸與SaaS死亡螺旋將重塑經濟
Inside The Industry That Powers Every Business In America | Deep Dives with a16za16z Deep DivesTreeline’s Peter Doyle targets the $100B MSP market with humans, automation, and AI—not pure-play SaaS.
Outsmarting Uber: Why Bolt Wins in Europe | Deep Dives with a16za16z Deep DivesBolt scaled from Estonia to 52 countries, survived near-bankruptcy, and now targets robotaxis with 12x less capital than Uber
Why AI Agents Need Context | Deep Dives with a16za16z Deep DivesFivetran’s George Fraser says AI agents need centralized data, while SAP API lockouts and the SaaS apocalypse threat remain overblown.
EP312. SPCX 併購 Cursor、Fable 5 還在牢裡、AI 產業的新利空 | M觀點M觀點SpaceX以600億美元併購Cursor,Anthropic受禁令困擾,AI從Token Maxing轉向預算最佳化