📰 Dev.to · George Belsky

Your agent ran 47 steps of a 50-step pipeline. Then it crashed. The state is gone. Every framework says you should have configured checkpointing. Why isn't dura

Your AI agent went rogue at 3am. It's running on multiple instances across regions. There's no terminal to Ctrl+C. You need a kill switch that works in under 1

Adding a human approval step to an AI agent workflow means building a notification service, reminder scheduler, escalation chain, and webhook handler. Or you ca

Temporal is the go-to for durable execution. But it requires a cluster and forces determinism constraints on your workflow code. Here's an alternative that give

One runaway retry loop. 10,000 API calls. $130 in LLM costs. No rate limit fired because you never built one. Here's how to add centralized rate and cost limiti

Container is green. Process is running. But your agent stopped processing work 2 hours ago. Heartbeat monitoring catches what health checks miss.

Your agents are running across 4 machines. One dies. No alert. No log. You find out 3 hours later from a customer complaint. Here's how to fix that.

AI agents go rogue. They send thousands of emails, make unauthorized API calls, burn through budgets. You need a kill switch that works in under 1 second.

AI agents call LLMs. LLMs cost money per token. Nobody tracks it per agent. One runaway loop and your OpenAI bill is a disaster.

AI agents call LLMs. LLMs cost money per token. Nobody tracks it per agent. One runaway loop and your OpenAI bill is a disaster.

AI agents go rogue. They send thousands of emails, make unauthorized API calls, burn through budgets. You need a kill switch that works in under 1 second.

AI agents call LLMs. LLMs cost money per token. Nobody tracks it per agent. One runaway loop and your OpenAI bill is a disaster.

AI agents go rogue. They send thousands of emails, make unauthorized API calls, burn through budgets. You need a kill switch that works in under 1 second.

Google's A2A protocol handles agent communication. But crash recovery, retries, timeouts, and human approval gates? That's still on you. Unless you add a lifecy

Exponential backoff, jitter, dead letter queues, idempotency keys, HMAC verification - all to deliver one message reliably. There are better options now.

Temporal gives you durable execution. But adding a human approval step mid-workflow requires building a signal handler, notification service, and UI. There's a

AI agents do multi-step work. They crash. The state is gone. Here's how to make agent operations durable without writing checkpoint code.

AI agents get stuck waiting for humans. There's a better pattern than blocking - async approval with reminders, escalation, and timeout.