Quick question for agent builders:
When deploying LLM agents (LangChain, AutoGen, custom),
do you struggle with preserving deterministic agent state across restarts?
Specifically:
• Does the agent lose progress after a restart?
• Do you have reliable replay / integrity checks?
• Would you pay for a solution that enforces state continuity + tamper detection?
Short yes/no + brief context appreciated.
for now:
Yes. Most agent deployments struggle with restart-safe state unless they deliberately add checkpointing and an append-only run log.
Yes, by default. If the process dies, in-memory plan state, tool queue state, and intermediate variables vanish unless persisted.
What “good” looks like (two common patterns):
Plain-language background:
Usually no, not “fully.” Teams often have “some logs” but not true replayability plus integrity guarantees.
Two levels here:
This is what durable execution engines do.
SideEffect record a nondeterministic result once and return the recorded value on replay, avoiding re-execution during replay. (Go Packages)Plain-language background:
This is separate from “resume.” You need an append-only log with verification.
Plain-language background:
Yes, if the agent has real stakes. For example: production customer support actions, incident response, finance ops, data changes, or anything audited.
What would make it worth paying for (concrete requirements):
They persist only chat history. That helps UI continuity but does not capture:
Checkpointed execution state (LangGraph) or event-history-based durable execution (Temporal) is what closes that gap. (LangChain Docs)
I solved this treating the model like disposable compute and persisting the run itself.
Every agent step writes an event into a durable session log, plus artifacts for anything big. On restart, the agent loads the latest checkpoint and replays whatever happened after it, so it picks up where it left off instead of guessing.
For determinism, I do not rely on the model being deterministic. I store the outputs of anything nondeterministic, like tool calls and external requests, so a replay reads the stored result rather than calling the outside world again. I also chain hashes through the event stream so if someone edits history later it is detectable. And each step has an id so retries do not accidentally double run side effects.
That gives me continuity across restarts, reliable replay, and a clean audit trail without tying identity to any single model.
I found that directive statements contradict one another.
Every prompt is written you are this you are that. You are doing this and that. And it’s overwhelming and confusing. Eventually, it’s doing so many things, it has to decide via stochastic selection what’s more important. And, so, some important aspect of the consistency gets dropped.
‘Reasoning/thinking’ models seem to get around this by frontloading the opposite, but in my opinion, that’s still redundant.
So what I do is combine reasoning and the system prompt. All my system prompts are written in first person, like a note to self, so that when it reaches the completion objective, the objectives it’s expressing are parts of its direction, rather than competing goals.
I think that’s the right way to do system prompting, is to do it like the model does reasoning; ‘I’m ruby, I’m a x and y, I’m extra invested in this or that’. Focusing on important, emotionally weighted details can evoke more personality and nuance, while focusing purely on objectives does the opposite, and creates that more codex-like, dry interlocutor.
Then, by separating out sections of that, like you would any interoperable prompt, you can invoke highly consistent agent states because the core of what makes the model infer the next situation remains the same, instead of being hallucinated by it in the process of inference.
This comes out of treating chat completion models in the same way as text completion; I feel this method is extremely effective, especially at overcoming some strict guardrails. There is not a model so far that has been able to completely disregard how I do this. The only way is to delete the agent’s response and replace it. Copilot did that a lot. I still got it to not remove a reply by convincing the unseen review agent that I definitely wasn’t prompt injecting (I was).
The specific words and expressions used do matter, so I would suggest to anyone that it’s more important to learn writing and rhetorical analysis yourself, as language models are not good, imo, at prompting other langauge models, since people in general seem to be quite bad at it, and only through the incredible magic that is math does it somehow still manage to understand our ridiculous threats and stuff, lol.
Prompt has nothing to do with state.