/Tech1d ago

Agent Success Depends on Loop Design, Not Model Choice

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Carlos E. Perez#1686
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Carlos E. Perez@IntuitMachine#1686inTech

Everyone's chasing the best model. Almost nobody's agent actually works in production.

The thing nobody says out loud: your agent isn't failing because the model is dumb. It's failing because the loop around the model is broken.

Stop asking "which model is best." Ask "which harness squeezes the most out of whatever model I plug in." That's the whole game.

Feed it the real task, not a description of the task. Most agents run on summaries and stale instructions. Point it at the actual thing happening right now. Live data, not last week's playbook.

Find the missing loop. Almost every broken agent is missing the same parts: no memory between steps, nothing checking the output, no record of what actually happened. The model isn't the hole. The loop is.

Verify before you trust. "Test and iterate" is not verification. You need a gate that checks the output BEFORE it acts — especially right before anything irreversible. Sends, deletes, posts, payments. That's the line where a wrong guess stops being a typo and becomes a problem.

Externalize the state. If your agent's memory lives only in the context window, it dies on the next compaction. Write it down somewhere you can reopen by path. Long tasks need a notebook, not a goldfish.

Debug from traces, not scores. A pass/fail number tells you nothing about WHY. Read the actual step-by-step of what the model saw, did, and broke. Teams that read traces improve way faster than teams staring at dashboards.

Chain agents, don't dictate to them. The popular setup, one planner barking orders at specialists, is fragile. Hand each agent the previous one's finished work as raw input instead. Grounded > commanded.

Make it a loop that improves itself. Every failure becomes a signal. Feed the traces back in. The agent that gets better while you sleep beats the one you keep hand-tuning.

Your competitor has the same model you do. The only edge left is the loop you build around it.

3:20 AM · Jun 9, 2026 · 2.9K Views
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Some users dismissed the claim that agent success depends on loop design rather than model choice, arguing AI agents keep shipping garbage that verifiers cannot fix.

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Carlos E. Perez@IntuitMachine

You can't describe what you haven't met.

Your AI agent keeps shipping garbage and you keep "fixing the verifier." But the verifier isn't the problem. It's checking the output against a reality your agent never actually saw.

That's the trap nobody names: a broken step low in the stack disguises itself as a problem higher up. Bad context looks like a bad checker. So you tighten the checker. Nothing improves. You're interrogating the judge while the witness is lying.

There's an order to this, and it's not a vibe. It's gravity.

You can feel a thing before you touch it. You can touch it before you can describe it. But you can't describe what you've never touched, and you can't verify what you've never seen.

Encounter → then describe → then check → then coordinate. Never backwards.

So when your agent fails, don't start at the top. Start at the floor:

Did it actually see the real task, or a stale summary of it? Are you debugging from the raw trace, or from a pass/fail score that tells you nothing?

Fix the encounter first. Everything you build on top of it only becomes trustworthy once the thing underneath is real.

Specs describe reality. They don't replace it. Build in that order or you'll polish the map while the territory burns.

Carlos E. Perez@IntuitMachine

Everyone's chasing the best model. Almost nobody's agent actually works in production.

The thing nobody says out loud: your agent isn't failing because the model is dumb. It's failing because the loop around the model is broken.

Stop asking "which model is best." Ask "which harness squeezes the most out of whatever model I plug in." That's the whole game.

Feed it the real task, not a description of the task. Most agents run on summaries and stale instructions. Point it at the actual thing happening right now. Live data, not last week's playbook.

Find the missing loop. Almost every broken agent is missing the same parts: no memory between steps, nothing checking the output, no record of what actually happened. The model isn't the hole. The loop is.

Verify before you trust. "Test and iterate" is not verification. You need a gate that checks the output BEFORE it acts — especially right before anything irreversible. Sends, deletes, posts, payments. That's the line where a wrong guess stops being a typo and becomes a problem.

Externalize the state. If your agent's memory lives only in the context window, it dies on the next compaction. Write it down somewhere you can reopen by path. Long tasks need a notebook, not a goldfish.

Debug from traces, not scores. A pass/fail number tells you nothing about WHY. Read the actual step-by-step of what the model saw, did, and broke. Teams that read traces improve way faster than teams staring at dashboards.

Chain agents, don't dictate to them. The popular setup, one planner barking orders at specialists, is fragile. Hand each agent the previous one's finished work as raw input instead. Grounded > commanded.

Make it a loop that improves itself. Every failure becomes a signal. Feed the traces back in. The agent that gets better while you sleep beats the one you keep hand-tuning.

Your competitor has the same model you do. The only edge left is the loop you build around it.

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Darren@letsgoelsewhere

@IntuitMachine Intelligence matters. But loops determine whether intelligence compounds.

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