Picture this. You have a multi-stage AI pipeline. Maybe it starts by pulling a massive PDF from a bucket, then it hits an OCR model, then it sends that text to an LLM to extract specific line items, and finally, it hits another model to categorize those items for an accounting database. It sounds simple on a whiteboard. But here is the million-dollar question: where does that data actually live in the three seconds between the OCR finishing and the LLM starting?
It is the plumbing problem of the AI era. Everyone wants to talk about the shiny frontier models or the prompt engineering, but if you are building production systems, the "where" and "how" of data movement between those stages is what determines if your system is a brittle prototype or a resilient enterprise tool. Herman Poppleberry here, by the way, and I have been obsessed with this specific layer of the stack lately because it is where most "AI agents" actually fall apart.
Today’s prompt from Daniel is about exactly that—the practical architecture of state management in multi-step AI pipelines. Daniel wants us to dig into the trade-offs between in-memory passing, databases, Redis, and message queues. And he makes a really sharp distinction right out of the gate that I think we should highlight. This isn't about conversational memory. This isn't about your chatbot remembering that you like blue shoes. This is about the execution state within a single, complex run.
That is such a vital distinction. When people hear "AI memory," they immediately think of things like Mem0 or vector stores for long-term retrieval. But what we are talking about today is more like the "registers" in a CPU or the "state" in a functional program. It is the context that must survive just long enough for the pipeline to complete its mission. By the way, quick shout out to the tech behind us today—Google Gemini 3 Flash is actually writing our script for this episode.
It is funny because Gemini probably has some very strong opinions on how its own context window is managed. But let’s get into the "why" of this problem. If I am just writing a Python script, I just pass a variable from function A to function B. Why is that not enough once we move into a production environment?
In a perfect world where servers never crash, networks never lag, and LLM providers never return a five-hundred error, in-memory passing is king. It is essentially free. You are just passing a pointer in RAM. There is zero latency, zero infrastructure cost, and zero complexity. But the second you move to a distributed system—where Stage A might run on a GPU cluster and Stage B runs on a standard CPU worker—that "memory" is gone. You can't just pass a Python object across the network without serializing it and putting it somewhere.
Right, and even if you are on one machine, if Stage Three of your five-stage pipeline takes thirty seconds because the LLM is doing some heavy "thinking" or reasoning, and the process gets killed or the pod restarts, you’ve lost everything. You have to start from square one. And if Stage One and Two cost you five dollars in tokens, you just set five dollars on fire.
That is the "cost of re-compute" argument for durable state. It is basically insurance. If you write the output of Stage Two to a database or a persistent store before starting Stage Three, you have created a checkpoint. If Stage Three fails, you don't go back to the beginning; you just "hydrate" the state from your store and try again.
So let's look at the menu of options Daniel laid out. We have in-memory at one end—fast but volatile. Then we have things like Redis. I know you’re a fan of Redis for this. Why is a key-value store often the "sweet spot" for intermediate state?
Redis is the high-performance middle ground. It is an in-memory store, so it is incredibly fast—we are talking sub-millisecond latency for reads and writes. But unlike your local Python variables, it's a separate service. If your worker crashes, the data stays in Redis. It allows for what we call multi-agent coordination. If you have three different agents working on bits of the same problem, they can all read from and write to a shared Redis key. The downside is that it’s usually ephemeral by default. You have to manage time-to-live settings, or TTLs, so you don't fill up your expensive RAM with "state" from a pipeline that finished three weeks ago.
It’s like a digital whiteboard. Everyone can see it, it’s fast to write on, but eventually, someone has to come by with an eraser so you don't run out of space. Now, compare that to the heavy hitter: the traditional database. Postgres, SQLite, or even a NoSQL store. When does the latency hit of a database become worth it?
You use a database when you need an audit trail or when the data has a complex structure that you might want to query later. If you are building a legal document processing pipeline, you don't just want the final summary. You might want to be able to go back six months from now and see exactly what the raw OCR looked like for a specific page in a specific run. Databases give you durability and "queryability." The trade-off is the I/O overhead. Every time you write a stage's output to a database, you are looking at maybe fifty to a hundred milliseconds of latency once you factor in the network round trip and the disk write. In a twenty-stage pipeline, that adds up to two seconds of just "waiting for the database."
Two seconds feels like an eternity in a real-time application, but in a background batch process that takes ten minutes anyway, it's a rounding error. I think that’s a key point—the "right" architecture depends entirely on the user's expectation of speed. But what about the really big stuff? Daniel mentioned cloud volumes and temporary files. I assume you aren't stuffing a four-gigabyte video file into a Redis key.
Definitely not. That is where you run into "state bloat." If your pipeline involves heavy assets—high-res images, audio files, large datasets—you don't pass the asset itself. You pass a "reference." Stage One uploads the video to an Amazon S3 bucket or a cloud volume and then passes a string—the URI or the file path—to Stage Two. Stage Two then pulls only the bits it needs. It’s much safer for memory management, but it adds another layer of I/O complexity. You have to make sure your cleanup logic is bulletproof, otherwise, you end up with "storage rot"—thousands of orphaned temp files in S3 that you are paying for every month.
I’ve seen those S3 bills. They are not pretty. It's like having a house full of half-finished craft projects that you’re too afraid to throw away because you might need them later. Let’s talk about the "glue" that moves this state around. Daniel mentioned message queues like RabbitMQ or Kafka. That feels like a very "big tech" solution. Is that overkill for most AI workflows?
It depends on your scale. Message queues are brilliant for decoupling. If Stage One is producing data faster than Stage Two can process it—which happens a lot when you are hitting rate-limited LLM APIs—a queue acts as a buffer. It guarantees delivery. If a worker picks up a message and dies, the queue realizes the task wasn't "acknowledged" and puts it back for another worker to grab. It’s the gold standard for reliability, but the trade-off is "traceability." It can be very hard to follow a single request through a complex web of queues and workers unless you have really sophisticated logging.
It’s the difference between a relay race where you see the baton move from hand to hand, and a postal system where you drop a letter in a box and just trust the system to get it to the destination. Speaking of relay races, I want to dig into the frameworks Daniel mentioned—LangGraph, Temporal, and Prefect. These seem to be the "opinionated" ways of doing this in 2026. Herman, you’ve been playing with LangGraph lately. How does it handle this "state" problem differently than just a bunch of nested if-statements?
LangGraph is fascinating because it treats the entire pipeline as a stateful graph. You define a "StateSchema"—basically a typed dictionary of all the variables your pipeline needs. Every time the "baton" passes to a new node in the graph, that node gets the current state, does its thing, and returns an "update" to the state. What makes it special is the built-in "checkpointer." You can tell LangGraph to save that state to a database after every single node. This enables what they call "time travel." You can literally pause a running pipeline, inspect the state, change a value, and then resume it.
That sounds like a dream for debugging. I can't tell you how many times I've had a pipeline fail on step eight, and I have to spend twenty minutes re-running steps one through seven just to see why step eight is acting up.
And that leads us to Temporal, which is even more hardcore about this. Temporal provides what they call "Durable Execution." They don't just save the state; they record every single "side effect." If your code calls an API, Temporal logs the result. If your server dies mid-function, a new worker picks up the task, and Temporal "replays" the history. It doesn't actually re-run the API call—it just looks at the log and says, "Oh, last time you called this, you got this result," and it feeds that back into the code until it reaches the exact line where the crash happened. It makes your code essentially "immortal."
Immortal code. That’s a bold claim. But I imagine the performance overhead for that kind of logging is non-trivial?
It is. You wouldn't use Temporal for a high-frequency trading bot. But for a "long-running" AI workflow—like an agent that has to browse the web, write a research paper, and then email it to a human for approval over the course of three hours—Temporal is the only way to sleep soundly at night.
So we have this spectrum. On one end, you have the "cowboy" approach: in-memory variables, no checkpoints, if it fails, it fails. On the other, you have the "immortal" approach with Temporal or LangGraph’s persistent checkpointers. I want to talk about the "Checkpoint and Resume" strategy specifically. Daniel asked: when a stage fails, do you replay or resume? From a cost perspective, especially with these high-end reasoning models like the O1 series or whatever the latest heavy-hitter is, replaying the first half of a chain could cost you ten dollars.
This is where "idempotency" becomes the most important word in your vocabulary. If you are going to resume a pipeline, every stage needs to be idempotent—meaning if you run it twice with the same input, it doesn't cause problems. Imagine a pipeline that charges a customer's credit card and then sends a confirmation email. If it crashes after charging the card but before sending the email, and you just "resume" it without care, you might charge them again. Durable state allows you to check: "Did I already complete the 'Charge Card' stage for this Run ID? Yes? Okay, skip to 'Send Email'."
It’s the difference between a "stateless" function that doesn't know what happened five seconds ago and a "stateful" workflow that has a memory of its own progress. I think a lot of developers coming from the world of simple web APIs are getting slapped in the face by this. In a web API, the request is short. In an AI pipeline, the "request" might last five minutes. The mental model has to shift from "request-response" to "workflow orchestration."
That is the big shift of 2026. We are moving away from "chains" and toward "graphs." A chain is fragile. If one link breaks, the chain is useless. A graph with durable state is resilient. You can have cycles, you can have loops where the LLM says, "Actually, I need to go back to step two and try again because this output doesn't make sense." If you are doing that in-memory, your call stack becomes a nightmare. If you are doing it with a stateful framework, it’s just another edge in the graph.
Let's look at a concrete case study to make this real. Say you’re building a real-time sentiment analysis tool for a live stream. Thousands of comments a second. You need to aggregate them, run them through an LLM to find trends, and update a dashboard. How are you handling state there?
In that scenario, speed is everything. I’m using Redis. I’d have a "buffer" stage that collects comments into a Redis list. Every five seconds, a worker pulls that list, sends it to the LLM, and writes the summary back to a "latest_trends" key in Redis. I don't care if I lose five seconds of data if a server crashes—it’s a live stream, it’s ephemeral anyway. The latency of a database would kill the "real-time" feel.
Okay, now flip it. You’re building an AI-powered mortgage processing system. It takes in fifty different documents, verifies income, checks credit scores, and generates a risk report.
Now I’m going full "durable." I want every single document extraction stored in a persistent database with a unique Run ID. I want to use something like Temporal or a very robust LangGraph setup with a Postgres checkpointer. If the "Credit Check" API is down for two hours, I want the pipeline to just "wait" and resume exactly where it was once the API is back. I don't want to re-process the applicant's tax returns for the tenth time just because of a network glitch. The cost of the tokens and the sensitivity of the data make "durable" the only logical choice.
I think one thing people miss is the "debuggability" aspect. When you have a complex pipeline and the final output is "hallucinated" or just wrong, you need to be able to "inspect the corpse." If everything was in-memory and the process finished, you have no idea where it went off the rails. You just see the bad result. If you have "state snapshots" in a database, you can look at the transition between Step Four and Step Five and say, "Aha! Step Four produced garbage, which confused Step Five."
That "state inspection" is the secret weapon of high-performing AI teams. They build internal dashboards where they can see the "state" of every active run. They can see a "visual" graph of the pipeline and click on any node to see exactly what the input and output were. It turns the "black box" of an AI agent into a transparent process.
It’s funny, we’ve spent all this time talking about the plumbing, but it really does come back to the human experience of building these things. It’s about confidence. If I’m a developer and I push a new version of a pipeline, I want to know that I can handle the "edge cases" of reality—the timeouts, the crashes, the weird inputs. Durable state gives you that safety net.
It really does. But I want to playing devil's advocate for a second. Is there such a thing as "too much state"? I’ve seen teams get bogged down trying to make everything "perfectly durable" and "perfectly traceable," and they end up with so much architectural overhead that they can't actually ship features. They spend all their time managing Kafka clusters and database schemas instead of improving the actual AI logic.
I call that "Infrastructure Narcissism." When the plumbing becomes more important than the water. There is definitely a point of diminishing returns. If your pipeline is three steps and takes two seconds, just use in-memory passing and a simple "try-except" block. Don't build a Temporal workflow for a script that summarizes a hundred-word email. You have to match the "weight" of the architecture to the "value" of the run.
That’s a great rule of thumb. What is the "value" of the run? If the run costs fifty cents in tokens and takes ten minutes of human-equivalent work, protect it like a precious heirloom. If it costs a fraction of a cent and takes half a second, treat it like a paper plate and just throw it away if it breaks.
Let's talk about the "State Bloat" problem again, because I think it’s a silent killer. In these LLM frameworks, there’s a tendency to just keep appending things to the "state" object. "Here’s the raw text, here’s the summary, here’s the translation, here’s the feedback on the translation." By step ten, your "state" object is ten megabytes. If you are writing that ten-megabyte object to a database twenty times per run, you are creating a massive amount of data noise.
You have to be disciplined about "state pruning." A good pipeline architecture should have a "cleanup" or "compaction" stage. After Step Three is done with the raw text, Step Four should maybe drop the raw text from the active state and just keep the summary. Or, like we mentioned earlier, move the raw text to a "cold" store like S3 and just keep the pointer. You want your "active state"—the stuff being passed between functions—to be as lean as possible. It helps with latency, it helps with memory usage, and honestly, it helps with the LLM's performance too, if you are feeding that state back into the prompt.
Oh, that’s a huge point! If your "state" becomes your "context window," and you haven't pruned it, you are wasting tokens and potentially confusing the model with irrelevant intermediate steps. It’s like trying to have a conversation while holding every single book you’ve ever read. Eventually, you’re going to drop one.
It’s the "lost in the middle" problem. LLMs are still better at focusing on the beginning and the end of a long context. If your state is a giant mess of intermediate logs, the model might miss the one crucial piece of data it needs for the current task. So, "State Management" isn't just a backend engineering problem; it’s a "Prompt Engineering" problem too.
So, looking ahead, where do you see this going? Daniel mentioned that frameworks are bringing more "structured opinions." Do you think we’ll see a "standard" emerge for how AI state is handled?
I think we are seeing a convergence. You see LangGraph adding features that look like Temporal, and you see orchestrators like Prefect adding better support for "fine-grained" LLM state. I think the "standard" will eventually be some form of "Functional Data Engineering." Every stage of an AI pipeline will be treated as a pure function: State In, New State Out. The "how" of where that state is stored—be it Redis, Postgres, or a specialized "AI State Store"—will become an implementation detail that you can swap out with a single config line.
That would be the dream. "I’m in dev mode, use in-memory. I’m in prod, use Redis. I’m in 'high-security' mode, use an encrypted Postgres instance." Total flexibility based on the environment.
And we are getting there. The tools are maturing so fast. But the fundamental trade-offs Daniel asked about—latency, reliability, cost—those are laws of physics. They aren't going away. You will always have to choose where you want to sit on that spectrum.
It’s been a fascinating deep dive. I think the big takeaway for me is that "state" is the "nervous system" of an AI agent. If it’s not well-designed, the agent might have a big "brain" (the LLM), but it won't be able to coordinate its limbs or remember what it was doing two seconds ago.
Well said. It’s about moving from "AI as a toy" to "AI as a reliable component of a larger system." And that transition happens in the plumbing.
Alright, let's wrap this up with some practical takeaways for the folks listening who are actually staring at a messy Python script right now. Herman, if you had to give three pieces of advice for someone architecting a new multi-step pipeline today, what would they be?
First, define your "State Schema" early. Don't just pass around random dictionaries. Use something like Pydantic or a typed class so you know exactly what is moving through your pipeline. It will save you a world of hurt when debugging. Second, identify your "High-Value Checkpoints." If a stage takes more than ten seconds or costs more than a few cents, write its output to a persistent store. Don't risk re-running it. And third, keep your "active state" lean. If a piece of data is no longer needed for the next step, prune it or move it to a "cold" store. Your memory and your wallet will thank you.
I’d add a fourth: think about "What if it fails?" at every single step. Don't treat a failure as a disaster; treat it as a planned-for state. If you have "Checkpoint and Resume" in your mental model from day one, you’ll build a much more resilient system than if you try to bolt it on at the end.
One hundred percent. It’s about building for the "real world" where things break, rather than the "happy path" where everything works perfectly.
This has been a great one. I feel like I understand the "why" behind these frameworks a lot better now. It’s not just "more complexity for the sake of complexity"; it’s a response to the very real challenges of running these models at scale.
It’s the "industrialization" of AI. We are moving out of the "artisan" phase and into the "engineering" phase. It’s exciting to see.
Huge thanks to Daniel for the prompt. This is one of those topics that sounds "dry" on the surface but is actually the core of everything we are trying to build right now. And thank you to everyone for listening.
Definitely. This is where the real work happens.
We should also thank our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power this show—their serverless infrastructure is actually a great example of how to handle some of these scaling and state challenges in a modern way.
This has been My Weird Prompts. If you are finding these deep dives useful, leave us a review on Apple Podcasts or Spotify. It’s the best way to help other "AI plumbers" find the show.
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Until next time, keep your state clean and your latency low.
Catch you in the next one. Bye.
See ya.
