Aladdin Kaya

Observing production drift
retry ↑ latency ↑ cycle_time ↓
Building in public →

[Aladdin Kaya @AladdinKayaa]

I noticed something simple:

systems don’t fail suddenly.
they drift.

retries ↑ latency ↑ cycle_time ↓

Built a small demo that pauses before failure compounds.

Still early, but it works:
https://t.co/oeukf6Nvnx

[Aladdin Kaya @AladdinKayaa]

The challenge is no longer building intelligence.
The challenge is knowing when to trust it.

#AIInfrastructure #Verification #Governor

[Dario Amodei @DarioAmodei]

Alongside it, Anthropic is releasing a proposal for how governments can address the risks posed by frontier AI and a policy framework for job displacement, for which we intend to provide substantial financial backing. https://t.co/P0W2lIKbdY

[Aladdin Kaya @AladdinKayaa]

As agent systems scale, verification becomes infrastructure.

#AIInfrastructure
#Agents
#Verification

[Claude @claudeai]

Dynamic workflows in Claude Code are now generally available.

For complex tasks like codebase-wide bug hunts, Claude writes its own orchestration and runs subagents in parallel, verifying the work before it reaches you.

Read more: https://t.co/nbNpvkfRBZ

[Aladdin Kaya @AladdinKayaa]

@bcherny Self-verification increases autonomy.

It does not automatically guarantee trustworthiness.

[Aladdin Kaya @AladdinKayaa]

@yunta_tsai Capability compounds.

Understanding compounds faster.

The second one is harder to automate.

[Aladdin Kaya @AladdinKayaa]

@mattpocockuk Queues tell us where work is waiting.

They don’t tell us whether the system is actually making progress.

[Aladdin Kaya @AladdinKayaa]

Interesting result.

Accuracy improved dramatically after adding a deterministic execution layer.

The question may no longer be:

“Can the agent do the task?”

but:

“Can we trust how the task was completed?”

Reliability is becoming infrastructure

[Anthropic @AnthropicAI]

New Science Blog: Why has AI advanced faster in coding than in biology?

To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic.

How do we build infrastructure agents can use?
https://t.co/PQaNQ4GRJZ

[Aladdin Kaya @AladdinKayaa]

@garrytan Capability without reliable judgment eventually becomes a trust problem.

[Aladdin Kaya @AladdinKayaa]

@bcherny The interesting question isn’t whether a loop can run for days.

It’s whether we can tell when its judgment starts drifting during those days.

[Aladdin Kaya @AladdinKayaa]

@MicrosoftLearn I’m focusing on the layer between capability and judgment.

As agents become more autonomous, knowing when to stop, review, or escalate may become just as important as knowing how to act.

[Aladdin Kaya @AladdinKayaa]

The biggest AI story may not be AI replacing tasks.

It may be AI accelerating AI development.

That’s where the compounding begins.

[Aladdin Kaya @AladdinKayaa]

@mipsytipsy The capability leap is real.

The operational trust debt is real too.

[Aladdin Kaya @AladdinKayaa]

@emollick The hard part is no longer proving AI can generate value.

The hard part is governing reliability once that value scales across the organization.

[Aladdin Kaya @AladdinKayaa]

@garrytan The interesting shift is that prompts are slowly becoming trainable artifacts instead of static instructions.

Evaluation loops may become more important than prompt writing itself.

[Aladdin Kaya @AladdinKayaa]

@GoogleAIStudio Vibe coding governance layers before agents start vibe deploying themselves into production.

[Aladdin Kaya @AladdinKayaa]

@MicrosoftLearn Interesting seeing governance move from “security add-on” to core agent infrastructure.

The stack is shifting from:
build agents → govern execution.

[Aladdin Kaya @AladdinKayaa]

@garrytan Infinite compute doesn’t remove the need for judgment.

It amplifies the consequences of bad judgment.

[Aladdin Kaya @AladdinKayaa]

@claudeai As agents become more autonomous, capability stops being the bottleneck.

Long-running reliability becomes the real problem.

Not whether systems can act.

Whether they remain trustworthy while acting independently for hours.

[Aladdin Kaya @AladdinKayaa]

Governor v0.1 started as a simple operational drift signal.

What became obvious very quickly:

the hardest failures in agent systems are rarely visible crashes.

They’re silent degradations hidden behind healthy dashboards.

v2 expands deeper into that layer.

[Aladdin Kaya @AladdinKayaa]

This is exactly why operational drift matters.

As model capability commoditizes, judgment becomes the real bottleneck.

Most systems do not fail instantly.
They drift first.

https://t.co/oeukf6Nvnx

[Lenny Rachitsky @lennysan]

Automation is a lie. CLIs are over. The SaaSpocalypse is dumb.

A year ago @danshipper came on the podcast to predict where AI was heading. He was remarkably right—including the call that everyone was sleeping on Claude Code.

Dan has a unique lens into where things are going because his team at @every is possibly the most AI-pilled group of people in tech. I always learn a ton talking to Dan.

So I brought him back for round two. We'll score these in exactly a year:
🔸 Every company will have one “super-agent” in Slack.
🔸 Codex and Claude Code will become the new operating system for knowledge work.
🔸 The AI job apocalypse is not happening.
🔸 PMs and designers will thrive.
🔸 We will read way more AI-generated writing and we will like it.
🔸 "I would buy SaaS stocks right now."

Listen now 👇
https://t.co/wzxQ5bz49h

[Aladdin Kaya @AladdinKayaa]

Governor v0.1

Operational drift detection for agent systems.

Not a blocker.
A control point.

Most systems do not fail instantly.

They drift first.

https://t.co/0Ruj82hn3o