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Runtime

  • in Runtime
  • 7 min read

Fail an AI Agent Over to Another Node After a Crash

AI agent failover to another node is what you actually need when the machine running a long-lived agent dies mid-run — not a fresh agent that boots with an empty head, but the specific crashed process rebuilt where it left off, on a surviving box. Spinning up a replacement is easy. Reconstructing the process that had already scored 128 fraud alerts, advanced a queue cursor, and set its own pipeline_status = "degraded" — on a different machine, from durable evidence — is the hard part. This post shows how Promptise Foundry does that with a RuntimeCoordinator, static or registry discovery, and a shared journal, with no etcd or Consul in the picture. It also draws an honest line against the distributed runtimes other frameworks ship.

  • in Runtime
  • 7 min read

Governing Autonomous Agents: Budget & Health

Autonomous agent governance is the set of controls that answer the one question every tech lead asks before an agent runs unattended in production: what stops it from looping forever, calling an expensive API 4,000 times, or issuing a refund at 3am with nobody watching? A build_agent() call gives you a capable request-response agent, but the moment you let it run on a trigger with no human in the loop, "capable" is not enough — you need an envelope it cannot exceed. By the end of this post you will know the concrete controls Promptise Foundry ships for that envelope, and how to wrap your own agent in a budget plus a health policy with a real hard stop.

  • in Runtime
  • 8 min read

LangGraph Checkpointing vs Journal-Replay Explained

The honest way to frame langgraph checkpointing vs journaling is not "which one persists state" — both genuinely do — but "what unit each one persists": a LangGraph checkpoint saves the state of a single graph run, while a Promptise journal records the append-only event stream of a long-running, self-triggering process. That distinction sounds academic until a process reboots at 2am mid-run and you need it back exactly where it was. This post draws the line precisely, shows what each mechanism captures, and gives you a runnable script so you can watch a journal rebuild a crashed process from its own event log.

  • in Runtime
  • 6 min read

LangGraph vs Promptise: Long-Running Agents

If you are researching LangGraph vs Promptise for an agent that has to stay alive for hours or days — not just answer one request and exit — you are comparing two different layers of the stack, and it pays to be precise about which one you actually need. LangGraph excels at orchestrating the control flow inside a single invocation. Promptise's Agent Runtime targets the layer above the graph: an OS-level process that wakes on triggers, survives crashes, and stays under governance. By the end of this post you will be able to tell which layer your project needs, and you will have runnable code for a supervised, crash-recoverable agent.

  • in Runtime
  • 10 min read

Time-Travel Debug an AI Agent by Rewinding State

To time-travel debug an AI agent, you rewind its recorded history to the exact step before it did something baffling, rebuild the state it was actually looking at, and replay its decisions deterministically — instead of squinting at stdout and guessing. When a request/response function misbehaves you set a breakpoint and re-run it. An autonomous agent gives you neither luxury: it woke itself on a trigger you didn't watch, called an LLM whose output you can't reproduce, mutated its own state across dozens of invocations, and fired a side effect you now have to explain. This post shows how a Promptise journal plus its rewind and replay engines turn "why did it do that?" into a reproducible postmortem, and where that differs — honestly — from LangGraph's graph-state time-travel.