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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... show more

Mr. Buzzoni

28,217 subscribers

45,278 Aufrufe • vor 3 Monaten •via X (Twitter)

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17 Kommentare

Profilbild von Vipul Kumar Kewat

Vipul Kumar Kewatvor 3 Monaten

The shift from promptng every time to building a knowledge and workflows layer is a big change in how we use AI. The real advantage comes from systems that improve with repeated use.

Profilbild von Coscosmico

Coscosmicovor 3 Monaten

Raw model with no harness is still a wild horse. Loops and memory are the bridle.

Profilbild von karcharodon

karcharodonvor 3 Monaten

This is really has the full

Profilbild von VALIX

VALIXvor 3 Monaten

Thanks for alpha. I’ve been wanting to understand loop engineering for a while

Profilbild von Inflowzy

Inflowzyvor 3 Monaten

Raw models are impressive. Systems are what create value.

Profilbild von Amani

Amanivor 3 Monaten

Esto es contenido de calidad

Profilbild von 0xSlyth

0xSlythvor 3 Monaten

this is the blueprint

Profilbild von Akshay Gupta

Akshay Guptavor 3 Monaten

The blog is written by AI and it is super difficult to read through it. Need human!

Profilbild von yurii

yuriivor 3 Monaten

finally, someone can explain this very simply thanks

Profilbild von Yagami

Yagamivor 3 Monaten

Wild horse analogy hits perfect. That trace-score-patch loop is how agents actually get sharp.

Profilbild von DoDoDoDo

DoDoDoDovor 3 Monaten

give the man credit not your shitty posts

Profilbild von Secta

Sectavor 3 Monaten

loop engineering turns raw LLM power into calibrated runs

Profilbild von Max Bevza

Max Bevzavor 3 Monaten

need to watch this now

Profilbild von morph

morphvor 3 Monaten

Don't you regret about posting such an alpha content just for free?

Profilbild von Harley Lewis Foote

Harley Lewis Footevor 3 Monaten

raw LLM = wild horse. agent = horse with a job. this guy actually made it make sense 🔥 (@GoogleDeepMind)

Profilbild von Septimus

Septimusvor 3 Monaten

Yeah what is not mentioned is that loop engineering vs prompt = MASSIVE USE OF TOKENS. The total COST even using K3 it's still up using loops...this is a commercial dead end unless you have your own LLM/AI installation at home

Profilbild von NabiYok🍌

NabiYok🍌vor 3 Monaten

loops that write back into execution layers matter more than static weights ever will

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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

rari

20,934 Aufrufe • vor 3 Monaten

the loop is not the agent it is one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls repetition turns retries budgets exits success checks no progress use durable execution when the run must survive failure → GRAPH ENGINEERING controls topology nodes edges state branches checkpoints handoffs build stateful agents as explicit graphs → analyze the topology without putting a model in the loop → HARNESS ENGINEERING controls the blast radius tools permissions memory sandboxes evals traces humans isolate generated code from the host → test behavior before it reaches users → keep one trace across every model node and tool call → without loop control it never stops without graph structure you cannot see the path without harness control it can touch anything the loop lives inside the graph the graph lives inside the harness the model is not the system debug the layer that owns the failure save this

the loop is not the agent it is one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls repetition turns retries budgets exits success checks no progress use durable execution when the run must survive failure → GRAPH ENGINEERING controls topology nodes edges state branches checkpoints handoffs build stateful agents as explicit graphs → analyze the topology without putting a model in the loop → HARNESS ENGINEERING controls the blast radius tools permissions memory sandboxes evals traces humans isolate generated code from the host → test behavior before it reaches users → keep one trace across every model node and tool call → without loop control it never stops without graph structure you cannot see the path without harness control it can touch anything the loop lives inside the graph the graph lives inside the harness the model is not the system debug the layer that owns the failure save this

elune

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your agent is not the loop the loop is only the smallest layer LOOP vs GRAPH vs HARNESS ENGINEERING most teams still treat all three like one prompt that is why agent failures feel impossible to diagnose LOOP ENGINEERING controls iteration turns retries budgets evaluators exits stalled progress when the run keeps going forever the loop is broken make long-running work survive crashes and restarts → GRAPH ENGINEERING controls structure nodes edges state branches cycles joins checkpoints when the agent takes the wrong path the graph is broken make state and routing explicit → inspect the topology without asking another model to explain it → HARNESS ENGINEERING controls access tools permissions memory sandboxes evals traces humans when the agent touches the wrong system the harness is broken isolate generated code and tool execution from the host → put an eval gate before every model or prompt update → keep one trace across every node model and tool call → without loop engineering it never stops without graph engineering you cannot see the path without harness engineering it can reach anything the prompt lives inside the loop the loop lives inside the graph the graph lives inside the harness the model is the smallest box in the system debug the layer that actually owns the failure save this before the next agent rewrite

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97,613 Aufrufe • vor 1 Monat

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Morlex

51,952 Aufrufe • vor 2 Monaten

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NVIDIA and MIT researchers pointed Karpathy's loop at the agent harness itself and cut the API bill by 50-54% same models, same benchmark. only the harness around Claude Code and Codex changed the loop they ran on it: propose -> implement -> review -> test -> keep or discard the 4 changes that survived: 1. edit a file and run its test in one tool call 2. compact context only after a finished step, and only when it saves more than it costs 3. archive huge outputs and pass on a short excerpt plus a handle 4. hand log reading to a cheaper model, then check its evidence against the original the paper runs 15 pages. start with one fix, measure it, then add the next the same loop, aimed at an Obsidian second brain on Opus 5.5, is in the article below ↓

Mr. Buzzoni

14,964 Aufrufe • vor 6 Tagen

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Carnage

24,827 Aufrufe • vor 15 Tagen

Anthropic engineer: "At Anthropic, we don't write prompts anymore We build harnesses" In just 42 minutes, she shows how the Claude team builds harnesses that prompt themselves Agents → Harness → Loops → Graphs → Self-Improving Systems If this were a $500 course, people would call it one of the best agent courses of the year But it's completely free And it's better than most paid agent engineering courses Watch it today Then use the step-by-step harness engineering guide below

Anthropic engineer: "At Anthropic, we don't write prompts anymore We build harnesses" In just 42 minutes, she shows how the Claude team builds harnesses that prompt themselves Agents → Harness → Loops → Graphs → Self-Improving Systems If this were a $500 course, people would call it one of the best agent courses of the year But it's completely free And it's better than most paid agent engineering courses Watch it today Then use the step-by-step harness engineering guide below

rari

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