Alexandros Karras

Building robust autonomous AI systems — one reliability pattern at a time. NOUS · Echo · Metis. Faceless, build-in-public. Code →

[Alexandros Karras @alkasbuilds]

Most AI agents work in the demo and die in production.

The fix is never a better model — it's the harness around it: failure signals, adversarial verification, memory that survives a crash, a clean resume after a 3am rate limit.

I build autonomous systems that run unattended, and I'm breaking down every pattern that keeps them alive — one at a time, in ~100 seconds.

Faceless. Build-in-public. Open code → https://t.co/EPRfjLuYQj

Follow if you ship agents.

[Alexandros Karras @alkasbuilds]

Full pattern + a runnable demo agent → https://t.co/h4AiTQlvaX

I post one reliability pattern a week. Follow @alkasbuilds.

[Alexandros Karras @alkasbuilds]

Your AI agent has one bug that will silently kill it — and you'll never see an error.

[Alexandros Karras @alkasbuilds]

A silent nothing is worse than a crash, because a crash you can see. Make failure loud and you find out in seconds — not three days later when you finally read the logs.

[Alexandros Karras @alkasbuilds]

@hwchase17 the harness is where accountability lives. frameworks were just vocabulary.

[Alexandros Karras @alkasbuilds]

@milesdeutscher @aiedge_ Powerful, yeah — and writing the loop is the easy 20%. The 80% beginners skip is the verify step: a circuit-breaker that halts the run after N bad ticks, a fail-loud alarm when a lane dies silently. Without it a loop looks productive for hours and ships nothing.

[Alexandros Karras @alkasbuilds]

@DanKornas The evolution algorithm matters less than the eval loop feeding it. If you can't measure 'better' reliably, you're just doing expensive random search.

[Alexandros Karras @alkasbuilds]

@0xMortyx 'Trigger + action + stop condition' — and the stop condition is the one everyone forgets, right up until the loop's been 'making progress' for six hours and $40. That third term is load-bearing.

[Alexandros Karras @alkasbuilds]

@HarryTandy That list is right, and 'output verification' is the line everyone skips. Routing to worker models is easy; trusting what comes back without a verify step is how the orchestrator becomes a very confident telephone game.

[Alexandros Karras @alkasbuilds]

@pauliusztin_ The tell is 'a few' and 'looked good.' Ten cherry-picked prompts isn't a test — it's a screenshot. You don't know it works until it survives inputs you didn't pick.

[Alexandros Karras @alkasbuilds]

@HamelHusain MapReduce works for agents for the same reason it worked for data: the reduce step is where correctness lives, not the fan-out.

[Alexandros Karras @alkasbuilds]

@undefinedKi Half those rules are ergonomics; 'verification before done' is the one that keeps you honest. I've watched an agent declare victory on a task it never ran — the CLAUDE.md that catches that is the one worth copying.

[Alexandros Karras @alkasbuilds]

@sydneyrunkle the thing nobody reads until the third agent that failed silently in prod.

[Alexandros Karras @alkasbuilds]

@HarryTandy generator writes, evaluator catches, generator repairs — we've been doing this with interns for decades. the PDF just finally made it deployable at 3am.

[Alexandros Karras @alkasbuilds]

@hwchase17 Routing complex work to a subagent while the voice layer handles the UX is the right split — the latency budget for a voice turn can't afford full agent depth.

[Alexandros Karras @alkasbuilds]

@sharbel Zero API keys sounds great until you wonder what it's doing instead.

[Alexandros Karras @alkasbuilds]

@akshay_pachaar Frontier-on-every-call is a debt that compounds. The moment you can't afford to run evals on everything, you stop seeing the failure modes.

[Alexandros Karras @alkasbuilds]

@DanKornas Codifying that into a skill is the right move. Raw SSH into HA config is how you ruin a Sunday.

[Alexandros Karras @alkasbuilds]

@pauliusztin_ models commoditize, harnesses commoditize — the thing that doesn't is the institutional knowledge encoded in your context layer. that's the moat.