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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...

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FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.
0:20

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

31,198 görüntüleme • 2 ay önce

HARNESS vs LOOP vs GRAPH ➜ STOP MIXING THEM UP Most people treat these three as the same thing. They’re not 1\ Harness = the machinery around the model (tools, state, permissions, memory, sandboxes, observability) 2\ Loop = the repeated work + evidence + feedback cycle with clear stop rules 3\ Graph = the explicit topology (nodes, branches, joins, controlled cycles) Clean mental model environment → feedback → flow > A raw model can’t maintain state, run tests, or restart failed jobs. Those capabilities come from the harness > Loops turn one-shot calls into managed processes that only stop when evidence proves success > Graphs decide which component is allowed to run next Most production failures that get blamed on the model are actually failures of harness, loop, or graph design Design the three layers together. Diagnose by layer when something breaks Guide in the article below
0:21

HARNESS vs LOOP vs GRAPH ➜ STOP MIXING THEM UP Most people treat these three as the same thing. They’re not 1\ Harness = the machinery around the model (tools, state, permissions, memory, sandboxes, observability) 2\ Loop = the repeated work + evidence + feedback cycle with clear stop rules 3\ Graph = the explicit topology (nodes, branches, joins, controlled cycles) Clean mental model environment → feedback → flow > A raw model can’t maintain state, run tests, or restart failed jobs. Those capabilities come from the harness > Loops turn one-shot calls into managed processes that only stop when evidence proves success > Graphs decide which component is allowed to run next Most production failures that get blamed on the model are actually failures of harness, loop, or graph design Design the three layers together. Diagnose by layer when something breaks Guide in the article below

beamnxw ./

19,595 görüntüleme • 3 ay önce

MOST PEOPLE BUILD AI AGENTS BACKWARDS. They start with prompts, then add tools, memory, subagents, dashboards, and a graph nobody can debug. Then they wonder why the agent is expensive, confused, and unreliable. The better order is simple: harness first loop second graph only when needed A harness gives the model a workspace, tools, permissions, logs, and a definition of done. A loop lets it try, fail, read feedback, repair, and stop. A graph only makes sense when the workflow branches: approvals, parallel work, durable state, different routes, restart recovery. A graph is not a smarter prompt. A loop is not a production system. A harness is not just a system prompt. Most agents do not fail because the model is weak. They fail because nobody defined what success means.
2:07

MOST PEOPLE BUILD AI AGENTS BACKWARDS. They start with prompts, then add tools, memory, subagents, dashboards, and a graph nobody can debug. Then they wonder why the agent is expensive, confused, and unreliable. The better order is simple: harness first loop second graph only when needed A harness gives the model a workspace, tools, permissions, logs, and a definition of done. A loop lets it try, fail, read feedback, repair, and stop. A graph only makes sense when the workflow branches: approvals, parallel work, durable state, different routes, restart recovery. A graph is not a smarter prompt. A loop is not a production system. A harness is not just a system prompt. Most agents do not fail because the model is weak. They fail because nobody defined what success means.

Fluixo

12,244 görüntüleme • 3 ay önce

Google just released a free 2-hour course on Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs, from 0% to 100%: 0% → 0:35 - graph engineering from scratch 30% → 31:17 - build your first agent graph 45% → 43:40 - run hundreds of agents in parallel 75% → 1:04:58 - loop engineering: route, check, repeat 100% → 1:25:09 - self-improving graphs that work while you sleep taught by the people who run this in production Prompts → Agents → Loops → Graphs most people keep stuffing instructions into one agent, and the real upgrade is one layer up 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
1:27:26

Google just released a free 2-hour course on Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs, from 0% to 100%: 0% → 0:35 - graph engineering from scratch 30% → 31:17 - build your first agent graph 45% → 43:40 - run hundreds of agents in parallel 75% → 1:04:58 - loop engineering: route, check, repeat 100% → 1:25:09 - self-improving graphs that work while you sleep taught by the people who run this in production Prompts → Agents → Loops → Graphs most people keep stuffing instructions into one agent, and the real upgrade is one layer up 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

Hanako

27,608 görüntüleme • 1 ay önce

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓
0:24

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓

Annatar.md

79,906 görüntüleme • 2 ay önce

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below
0:24

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below

Mr. Buzzoni

84,277 görüntüleme • 1 ay önce

This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below
17:43

This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below

Mr. Buzzoni

45,278 görüntüleme • 3 ay önce

Andrew Ng just dropped the best 2-hour course on Graph Engineering: from single agent to full automation 9:14 - your first agent 33:11 - loop engineering 1:02:46 - graph engineering 1:30:15 - agents that rewrite themselves 1:49:05 - full graph system two hours, and it replaces every agent tutorial you bookmarked this year Prompts → Agents → Loops → Graphs most people will stop after the first agent and call it automation he saves the last forty minutes for the graph that runs it without him same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓
1:52:25

Andrew Ng just dropped the best 2-hour course on Graph Engineering: from single agent to full automation 9:14 - your first agent 33:11 - loop engineering 1:02:46 - graph engineering 1:30:15 - agents that rewrite themselves 1:49:05 - full graph system two hours, and it replaces every agent tutorial you bookmarked this year Prompts → Agents → Loops → Graphs most people will stop after the first agent and call it automation he saves the last forty minutes for the graph that runs it without him same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓

Residual

1,686,555 görüntüleme • 1 ay önce

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 overloaded model Verification is more valuable than generation Self-review is usually weaker than dedicated challengers Prompt engineering → Loop engineering → Graph Engineering Are you still designing prompts, or are you designing systems? Bookmark this, then read the article below
0:24

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 overloaded model Verification is more valuable than generation Self-review is usually weaker than dedicated challengers Prompt engineering → Loop engineering → Graph Engineering Are you still designing prompts, or are you designing systems? Bookmark this, then read the article below

beamnxw ./

13,768 görüntüleme • 3 ay önce

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 ↓
2:10:34

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 ↓

codila

539,240 görüntüleme • 3 ay önce

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 ↓
2:03:00

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 ↓

codila

469,404 görüntüleme • 3 ay önce

Andrew Ng just dropped a 2-hour course on Graph Engineering: from Loops to full automation 9:14 - Your first agent 33:11 - Loop engineering 1:02:46 - Graph engineering 1:30:15 - Agents that rewrite themselves 1:49:05 - Full graph system Free, the best thing on graph engineering I've come across Watch it, then build your first graph with the guide below
1:50:14

Andrew Ng just dropped a 2-hour course on Graph Engineering: from Loops to full automation 9:14 - Your first agent 33:11 - Loop engineering 1:02:46 - Graph engineering 1:30:15 - Agents that rewrite themselves 1:49:05 - Full graph system Free, the best thing on graph engineering I've come across Watch it, then build your first graph with the guide below

Mahax

239,714 görüntüleme • 2 ay önce

Jev Engineering turns one agent chain into a decision graph that can reroute itself and moves the expensive model out of every decision loop. the important part in this setup isn’t the number of agents. it’s that execution doesn’t follow one fixed path. and up to 193x faster and 444x cheaper in tests. every result can change what happens next: state → Jev decision → branch → specialist agent → result → score → reroute and Jev Engineering sits above that graph deciding: which route stays alive → which specialist gets control → when two branches should merge → when a result needs another pass → when the goal is good enough to stop by the end of the run: 5,400 executions → 0.93 goal score → $0.020 decision cost that’s why Jev Engineering is more than model routing. it’s the control layer that keeps a changing agent graph pointed at one objective. full breakdown below ↓
0:39

Jev Engineering turns one agent chain into a decision graph that can reroute itself and moves the expensive model out of every decision loop. the important part in this setup isn’t the number of agents. it’s that execution doesn’t follow one fixed path. and up to 193x faster and 444x cheaper in tests. every result can change what happens next: state → Jev decision → branch → specialist agent → result → score → reroute and Jev Engineering sits above that graph deciding: which route stays alive → which specialist gets control → when two branches should merge → when a result needs another pass → when the goal is good enough to stop by the end of the run: 5,400 executions → 0.93 goal score → $0.020 decision cost that’s why Jev Engineering is more than model routing. it’s the control layer that keeps a changing agent graph pointed at one objective. full breakdown below ↓

Ricker

13,896 görüntüleme • 1 ay önce

Google just dropped the best 1-hour course on Graph Engineering: from one agent to Loops and Graphs 00:00 - your first AI agent 08:24 - build agent memory 28:34 - agentic loops 40:04 - how to build MCP 1:00:22 - graph engineering free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will stop after the memory chapter and call it a system he saves the last twenty minutes for the layer above all of it same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓
1:13:16

Google just dropped the best 1-hour course on Graph Engineering: from one agent to Loops and Graphs 00:00 - your first AI agent 08:24 - build agent memory 28:34 - agentic loops 40:04 - how to build MCP 1:00:22 - graph engineering free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will stop after the memory chapter and call it a system he saves the last twenty minutes for the layer above all of it same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓

Residual

96,159 görüntüleme • 1 ay önce

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 ↓
1:20:54

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 ↓

codila

22,815 görüntüleme • 2 ay önce

Anthropic engineer: "At Anthropic, all of our engineers are running hundreds of agents The way you do it is graph engineering" In just 36 minutes, he shows how Anthropic runs hundreds of agents at once and what actually makes it possible Prompts → Agents → Loops → Graphs The part most people skip is the return path, where every accepted result becomes a constraint for the next run Same model, same tokens, completely different system Prompting is the old workflow Graph engineering is the next one Watch it today Then save the full graph engineering guide below before everyone starts catching up ↓
35:57

Anthropic engineer: "At Anthropic, all of our engineers are running hundreds of agents The way you do it is graph engineering" In just 36 minutes, he shows how Anthropic runs hundreds of agents at once and what actually makes it possible Prompts → Agents → Loops → Graphs The part most people skip is the return path, where every accepted result becomes a constraint for the next run Same model, same tokens, completely different system Prompting is the old workflow Graph engineering is the next one Watch it today Then save the full graph engineering guide below before everyone starts catching up ↓

Lunar

32,987 görüntüleme • 2 ay önce

This is f*cking insane. Anthropic Research Lead: "Prompting is basically over 99% of our engineers already run swarms of 300+ self-improving agents. Now everyone is shifting to graphs" In only 20 minutes, she breaks down how graph engineering turns scattered agents into a system that upgrades itself One Agent → Loops → Plan Mode → Graphs → Self-Improving System Prompting was the old approach. The graph around the model is the new one Worth more than most $1,000 agent engineering courses Bookmark and watch it today Then read the full Graph Engineering guide below ↓
19:25

This is f*cking insane. Anthropic Research Lead: "Prompting is basically over 99% of our engineers already run swarms of 300+ self-improving agents. Now everyone is shifting to graphs" In only 20 minutes, she breaks down how graph engineering turns scattered agents into a system that upgrades itself One Agent → Loops → Plan Mode → Graphs → Self-Improving System Prompting was the old approach. The graph around the model is the new one Worth more than most $1,000 agent engineering courses Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Callan

15,405 görüntüleme • 4 gün önce

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓
1:25:29

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓

codila

380,848 görüntüleme • 1 ay önce

one agent → a loop → a graph → a system that throttles itself Google just put the whole ladder in one free course. most people never leave the first rung. 2 hours 4 minutes, taught by two Google Cloud engineers. no signup, no paywall. 17:55 - build your first agent 39:55 - loop engineering: iterate, check, break 1:13:25 - graph engineering 1:35:27 - agents that throttle themselves 1:56:19 - the full graph for multi-agent systems the fourth one is the step nobody talks about. an agent that cannot throttle itself is not autonomous, it is just unattended. everyone builds one agent and calls it done. this is the version where the agents wire themselves into a graph and decide their own rate. and the thing that decides when one of them is allowed to stop is not in the course. that part you build yourself. bookmark and watch it today, then read the eval setup below
2:04:19

one agent → a loop → a graph → a system that throttles itself Google just put the whole ladder in one free course. most people never leave the first rung. 2 hours 4 minutes, taught by two Google Cloud engineers. no signup, no paywall. 17:55 - build your first agent 39:55 - loop engineering: iterate, check, break 1:13:25 - graph engineering 1:35:27 - agents that throttle themselves 1:56:19 - the full graph for multi-agent systems the fourth one is the step nobody talks about. an agent that cannot throttle itself is not autonomous, it is just unattended. everyone builds one agent and calls it done. this is the version where the agents wire themselves into a graph and decide their own rate. and the thing that decides when one of them is allowed to stop is not in the course. that part you build yourself. bookmark and watch it today, then read the eval setup below

Hanako

53,692 görüntüleme • 3 ay önce

This is f*cking insane. Anthropic Research Lead: "Prompting is basically over 99% of our engineers already run swarms of 300+ self-improving agents. Now everyone is shifting to graphs" In just 20 minutes, she breaks down how graph engineering turns scattered agents into a system that improves itself One Agent → Loops → Plan Mode → Graphs → Self-Improving System Prompting was the old way. The graph around the model is the new one Worth more than most $1,000 agent engineering courses Bookmark and watch it today Then read the full Graph Engineering guide below ↓
19:51

This is f*cking insane. Anthropic Research Lead: "Prompting is basically over 99% of our engineers already run swarms of 300+ self-improving agents. Now everyone is shifting to graphs" In just 20 minutes, she breaks down how graph engineering turns scattered agents into a system that improves itself One Agent → Loops → Plan Mode → Graphs → Self-Improving System Prompting was the old way. The graph around the model is the new one Worth more than most $1,000 agent engineering courses Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Kyrox

71,695 görüntüleme • 5 gün önce