Topic

Workflow Orchestration

All digests tagged Workflow Orchestration

Suggested route

Topic journey

Move from earlier context through the available material to the newest signals. This route is a reading aid, not a difficulty ranking.

Start here

Your agent architecture has a half-life of 6 months — Dan Farrelly, CTO, Inngest Jul 21, 2026 · 19:20 AI agent architectures have a short half-life due to rapid technological change. To build resilient systems, focus on decoupling into three layers: Execution, Context, and Compute. The stable core must be the durable execution layer.

Build context

Inside the Dark Factory: AI That Ships Code Solo Jul 28, 2026 · 58:39 Learn how Tessl's Dark Factory ships 70% of its code autonomously using AI agents, sophisticated verifiers, and a robust orchestration layer. Stanford CS329A Self-Improving AI Agents | Part 1 | Course Overview Aug 3, 2026 · 1:09:42 This course covers LLM scaling laws, CoT reasoning, and the shift to agentic workflows. We explore techniques like RLHF, inference scaling (Large Language Monkeys), and building systems with verifiers. Grok Bot Is The First AI Agent You Just Install. Is It Worth $200? Aug 14, 2026 · 18:56 Grokbot is a consumer multi-agent product that simplifies complex automation. It operates on a single, dedicated cloud computer, solving integration pain points for non-technical users.

Recent signals

We Trust AI Agents That Break Every Day - Shy Ruparel, Temporal Oct 29, 2026 · 21:32 AI agents are powerful but fragile. This talk details the four gaps—from usage vs. production to seeing vs. recovering—and demonstrates how Durable Execution solves state loss and cost issues in long-running workflows. Decision models take off & Anthropic bans cruelty to Claude Oct 16, 2026 · 32:04 The panel discusses the 'cambrian explosion' of decision models, which bypass LLMs' auto-regressive loop for faster, programmatic classification. They also debate the efficiency limits of multi-agent systems. Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya Oct 8, 2026 · 16:50 Running AI agents in production requires separating the 'brain' (LLM planning) from the 'hands' (deterministic harness). This approach treats agent harnesses as late-bound sagas, ensuring reliability and durability for complex, long-running workflows.

Browse view