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GRAPH ENGINEERING: STOP ADDING AGENTS AND START DESIGNING SYSTEMS More agents won’t save you if every step depends on the previous one Graph Engineering forces the better question “Where does the work naturally split? When it does, parallel researchers + independent verifiers + a final merge consistently outperform one... show more

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13,723 görüntüleme • 26 gün önce •via X (Twitter)

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Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓

Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓

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Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below

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Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that builds itself: 10:16 - Build your first AI agent 41:05 - Master prompt engineering 54:45 - Turn agents into graphs 1:20:10 - Run loops inside agent graphs 1:43:33 - Build a graph that builds itself Most people ship one agent and stop there. Google is teaching everything after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read how to run 1,000 agents from one prompt below ↓

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that builds itself: 10:16 - Build your first AI agent 41:05 - Master prompt engineering 54:45 - Turn agents into graphs 1:20:10 - Run loops inside agent graphs 1:43:33 - Build a graph that builds itself Most people ship one agent and stop there. Google is teaching everything after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read how to run 1,000 agents from one prompt below ↓

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Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

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Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, graphs are the next step" "In 3-6 months, everyone will be using self-improving graphs, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems with graphs Agents → Feedback → Memory → Graphs → Self-Improving Systems Worth more than most $500 graph engineering courses Bookmark and watch the talk today Then read the full article on graph engineering below ↓

Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, graphs are the next step" "In 3-6 months, everyone will be using self-improving graphs, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems with graphs Agents → Feedback → Memory → Graphs → Self-Improving Systems Worth more than most $500 graph engineering courses Bookmark and watch the talk today Then read the full article on graph engineering below ↓

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Anthropic engineer: "You don't need better prompts. You need graph engineering that makes agents remember everything." In 25 minutes he shows how engineers at Anthropic wire agents into a graph where each has a job, they run in parallel, verify each other, and share a memory that never resets. This beats any paid course on building agents I've seen. Watch it, then read the full guide on graph engineering below.

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Most people lump prompt engineering, context engineering, and graph engineering together. Here's the simple breakdown: - Prompt engineering: how you ask the AI a better question. - Context engineering: how you give the AI better information. - Graph engineering: how you design the work around the AI so it stops living inside one giant chat. Example: researching a startup idea. The chat way: - One model, one pass. - It decides what matters - Researches the market - Interprets the evidence - Writes the recommendation - Grades its own confidence. The graph way: - A planner breaks the question into angles - 5 researchers split up: customer, competitors, distribution, pricing, risks - A skeptic tries to kill the weak findings - A merger turns what survives into a one-page recommendation - You approve before you act Same written report. Completely different work behind it. Think: - Prompt = the question - Context = the information - Graph = the workflow Design the workflow. That's where it clicks.

Most people lump prompt engineering, context engineering, and graph engineering together. Here's the simple breakdown: - Prompt engineering: how you ask the AI a better question. - Context engineering: how you give the AI better information. - Graph engineering: how you design the work around the AI so it stops living inside one giant chat. Example: researching a startup idea. The chat way: - One model, one pass. - It decides what matters - Researches the market - Interprets the evidence - Writes the recommendation - Grades its own confidence. The graph way: - A planner breaks the question into angles - 5 researchers split up: customer, competitors, distribution, pricing, risks - A skeptic tries to kill the weak findings - A merger turns what survives into a one-page recommendation - You approve before you act Same written report. Completely different work behind it. Think: - Prompt = the question - Context = the information - Graph = the workflow Design the workflow. That's where it clicks.

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Google just released free 2-hour course on full Graph engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 10:16 - build your first agent 40% → 41:05 - Prompt engineering 50% → 54:45 - Graph engineering 75% → 1:20:10 - Loop engineering (with graphs) 100% → 1:43:33 - the graph that builds itself most people ship one agent and stop - this is everything after watch it today - then read how to run 1000 agents from one prompt below ↓

Google just released free 2-hour course on full Graph engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 10:16 - build your first agent 40% → 41:05 - Prompt engineering 50% → 54:45 - Graph engineering 75% → 1:20:10 - Loop engineering (with graphs) 100% → 1:43:33 - the graph that builds itself most people ship one agent and stop - this is everything after watch it today - then read how to run 1000 agents from one prompt below ↓

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Anthropic engineer: "You don't need better prompts. You need graph engineering: memory that stays, so your agent never forgets anything." In 28 minutes he shows what Anthropic does differently, how to build and structure work with agents. This beats any paid agent course I've seen. Watch it, then read the graph engineering guide below 👇

Anthropic engineer: "You don't need better prompts. You need graph engineering: memory that stays, so your agent never forgets anything." In 28 minutes he shows what Anthropic does differently, how to build and structure work with agents. This beats any paid agent course I've seen. Watch it, then read the graph engineering guide below 👇

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Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

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Head of Claude Code: "85% of our engineers are running dozens or hundreds of agents. The way you do it is graph engineering." In 40 minutes he explains how a single engineer now does the work of a whole team, how far this has gone inside Anthropic and where it goes next. This is something you can't skip if you don't want to be left behind. Watch it, then read the full guide on graph engineering below.

Head of Claude Code: "85% of our engineers are running dozens or hundreds of agents. The way you do it is graph engineering." In 40 minutes he explains how a single engineer now does the work of a whole team, how far this has gone inside Anthropic and where it goes next. This is something you can't skip if you don't want to be left behind. Watch it, then read the full guide on graph engineering below.

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the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below

the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below

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Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27 - Environment setup for agentic Graphs • 47:43 - Building Graph RAG agents • 1:25:14 - Agent memory bank with graphs Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27 - Environment setup for agentic Graphs • 47:43 - Building Graph RAG agents • 1:25:14 - Agent memory bank with graphs Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

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Head of Google AI: "75% of our engineers are running dozens or hundreds of agents. The way you do it is Graph Engineering." in 50-minutes, he shows how one engineer can do a whole team’s work, how far this has gone at Google, and what’s next. this is something you can't skip if you don't want to be left behind. watch it, then read the full guide on graph engineering below.

Head of Google AI: "75% of our engineers are running dozens or hundreds of agents. The way you do it is Graph Engineering." in 50-minutes, he shows how one engineer can do a whole team’s work, how far this has gone at Google, and what’s next. this is something you can't skip if you don't want to be left behind. watch it, then read the full guide on graph engineering below.

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Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

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Andrej Karpathy: "Prompting is fading away. Delete everything else and keep the graph." in 68 minutes at Stanford he explains how to build one and why it is the layer everything ends up becoming LLMs → Prompts → Agents → Graphs everything before the graph is a step the graph is where the system stops being steps most people are still perfecting the thing that goes away first same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then save the full graph engineering guide below

Andrej Karpathy: "Prompting is fading away. Delete everything else and keep the graph." in 68 minutes at Stanford he explains how to build one and why it is the layer everything ends up becoming LLMs → Prompts → Agents → Graphs everything before the graph is a step the graph is where the system stops being steps most people are still perfecting the thing that goes away first same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then save the full graph engineering guide below

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Google just released free 2-hour course on full agent engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 38:46 - build your first agent 30% → 54:46 - connect MCP tools 55% → 1:12:43 - Loop engineering: 4 ways to run agents 70% → 1:20:57 - Graph engineering 100% → 2:22:31 - the full system that works while you sleep most people build one agent and stop there - this is the full path to a system that runs without you from scratch watch it today - then read the full graph engineering playbook below ↓

Google just released free 2-hour course on full agent engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 38:46 - build your first agent 30% → 54:46 - connect MCP tools 55% → 1:12:43 - Loop engineering: 4 ways to run agents 70% → 1:20:57 - Graph engineering 100% → 2:22:31 - the full system that works while you sleep most people build one agent and stop there - this is the full path to a system that runs without you from scratch watch it today - then read the full graph engineering playbook below ↓

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Google just released 1-hour workshop on "Graph Engineering" for agentic systems from 0% to 100%: "loop agent is getting deprecated soon, so I’m going to focus on graph workflows" 0% → 8:01 - build your first agent from scratch 30% → 24:14 - Prompt engineering 55% → 44:48 - Graphs: kill the tree, draw the graph 80% → 1:00:47 - the router that never asks the model 100% → 1:19:49 - Parallel engineering: fan out without losing control most agent tutorials tell you to add another agent - this one deletes three of them and puts plain functions in their place watch it, open the lab, build the graph - then read the full architecture below ↓

Google just released 1-hour workshop on "Graph Engineering" for agentic systems from 0% to 100%: "loop agent is getting deprecated soon, so I’m going to focus on graph workflows" 0% → 8:01 - build your first agent from scratch 30% → 24:14 - Prompt engineering 55% → 44:48 - Graphs: kill the tree, draw the graph 80% → 1:00:47 - the router that never asks the model 100% → 1:19:49 - Parallel engineering: fan out without losing control most agent tutorials tell you to add another agent - this one deletes three of them and puts plain functions in their place watch it, open the lab, build the graph - then read the full architecture below ↓

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