Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses... that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.show more

Very useful watch, bro. Saw it first on my timeline - thanks for sharing.

Yeah bro, worth giving it 10 minutes if you’re building agentic systems - you won’t regret it.

looks like alpha from first seconds

its an alpha bro, if you are building agentic systems, thats for sure

pure alpha, bookmarked

thanks Yarchi, happy its useful for you bro !

useful share codez! i just bookmarked it

now, in essence, anyone can create agent cycles with the right approach

Yeah, if you want the “right loop,” you should definitely give it 10 minutes today.

yes, just some 10 minutes and that's it

mcp turns loops into systems

MCP definitely helps a lot to communicate with several services, that’s for sure. Connect your agent to any tooling in a few clicks.

Quite helpful codez but it's always harder than it sounds, I would try though

You just need to start taking the first steps - I’m sure it’s much easier than you think.

Okay noted I just started exploring hehe, we can be friends?

good thing i saw this video before buying the course

Yeah bro, definitely worth giving it 9 minutes today. Amazing watch on loop, trust me.

MCP is very helpful in automation, it's great

im using 20+ MCP on daily in my workflows, this is real alpha

model plus agent plus hooks plus mcp is the actual formula

yeah bro, most people stop on "model"

The setup is the part nobody shows you. Claude wired into your files, memory, and browser is where it goes from chatbot to operator:

This is the right direction, but “agentic loop” should not mean “let the model keep trying until it feels done.” A real loop is: trigger -> context -> agent run -> tool calls -> verifier -> state update -> next action The model is only one component. Hooks give lifecycle control. MCP gives the tool boundary. The loop becomes reliable when you add state, objective verification, permission limits, and hard stops. Minimum viable loop: - isolated branch/worktree - persistent state file or ticket - scoped MCP tools - test/typecheck/build gate - max iterations/tokens/time - human approval before merge/deploy The best first use cases are boring and machine-checkable: CI triage, dependency bumps, lint fixes, flaky test reproduction, broken-link checks. Bad first loops: auth, payments, infra, architecture, or anything where “done” is mostly judgment. The production prompt is not “agent, go fix this.” It is: “inspect this failure in an isolated workspace, change only relevant files, run these checks, stop after 3 attempts, open a draft PR only if verification passes, and escalate otherwise.”

STUPID “EXPLAINATION! You MUST demonstrate something I WOULD USE EACH MORNING! This is INANE, why would I wanna add two numbers each morning?

crazy that people still think AI agents are just fancy chatbots after watching stuff like this

The hooks part doesn't get talked about enough

model plus tools is useful, but hooks are what turn it into a system

Nice breakdown. Curious how practical it is at scale, but clear as day. Excited to see where this goes.

the mcp + hooks part is what most ppl miss. without proper tool definitions your loop just spins in circles

awesome

yeah nobody's arguing the model is the whole system. the diagram is fine. the "verifier" step doing all the real work is the part nobody wants to talk about.

The loop is the easy part. The hard part nobody teaches: designing the stop conditions and eval checks so it doesn't confidently loop in the wrong direction for 40 steps. An agent without good exit criteria isn't autonomous, it's expensive

Loops > bigger models. Compounding return is in the system architecture, not the weights. 10 min to learn, a career to master.

Loop plus model plus hooks plus MCP sounds clean in a diagram but the debugging experience when the loop hangs at step 7 of 20 is where every framework falls apart. What observability does this approach give you when the agent goes silent mid-loop?

hooks make the system click