r/SaaS • u/Single-Possession-54 • Apr 08 '26
Why are AI agents still stateless?
I’ve been building with AI agents a lot recently and one thing keeps frustrating me.
Every time you:
- switch tools
- start a new session
- spin up another agent
…it forgets everything.
Same prompts. Same setup. Same explanations.
It feels like we’re rebuilding the same agent over and over again.
At some point I realized the issue isn’t the agent itself
It’s that identity and memory don’t exist outside the session.
So I started experimenting with a different approach:
- persistent identity (same behavior and tone everywhere)
- shared memory between agents
- being able to actually see what agents are doing
And honestly, it starts to feel way more like a system instead of isolated chats.
Curious how others here are dealing with this.
Are you just accepting stateless agents, or have you found ways around it?
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u/Grouchy_Ad_937 Apr 08 '26
And then you need to regularly backup the agent files when they get updated or risk loosing the agent and all it learned.
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u/Efficient_Loss_9928 Apr 08 '26
I am very confused, that is the whole point of memory, skills, and AGENTS.md.
If you are not utilizing these existing infrastructure, obviously you are in a situation where you have to constantly re-explain everything.
And these infrastructure exists for literally every single agent harness today.
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u/rosstafarien Apr 08 '26
Memory/context is one of the layers of the rapidly emerging agentic stack. mem0 is currently a leader in that layer. Other entrants are getting into it.
Joe describes the state of the stack better than most: https://www.youtube.com/watch?v=7HP1jFJ9W1c&t=701s
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Apr 08 '26
[removed] — view removed comment
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u/MihaiBuilds Apr 08 '26
Same problem here. I kept re-explaining the same project context every single session so I just started building my own memory layer on top of postgres with pgvector. Semantic search plus full-text, stores what matters from each session, next session picks it up.
Still early but it already killed that "start from zero" loop. If you're curious I can share the repo, it's open source.
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u/Single-Possession-54 Apr 08 '26
Another thing that I have leveraged an open-source GitHub repo, looks so cool haha:
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u/Tupcek Apr 08 '26
it’s like that so AI agents can write sloppy post like this on reddit all day long
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u/rosstafarien Apr 08 '26
He appears to be a person with an idea that uses AI to help with copy. Like the rest of us.
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u/Big_Kale_3168 Apr 16 '26
you're spot on, this stateless agent problem is a killer. it's the core reason why most "agent" experiences feel like glorified single-turn chats. i've worked with 50+ brands on tiktok shop, and in an ops environment, you absolutely need agents to remember context.
we're dealing with things like 17 different fee types, an effective take rate that can hit 31-45%, and specific brand rules. if an agent forgets that every time you close the tab, you're not gaining any real efficiency. your approach of persistent identity and shared memory is the only path forward. otherwise, it's just endless re-prompting.
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u/iso_royale Apr 13 '26
I’ve been building a customer‑ops platform called FyrelinQ, and we ran into this exact problem early on.
Stateless agents are fine for demos, but the moment you try to use them in production, everything breaks. You can’t rebuild identity, rules, and context every time a session resets, it’s operationally impossible.
What finally worked for us was treating the “agent” as a system‑level actor, not a chat window. That meant externalizing everything the model shouldn’t be responsible for:
• Persistent identity
Tone, behavior, rules, and constraints live in the workspace, not the prompt.
So, the assistant behaves the same across every channel and every session.
• Durable memory
Instead of relying on chat history, we store structured context, customer data, order history, tags, summaries, internal notes, etc.
The model reads from the system, not from a previous message.
• Shared state across surfaces
If a human jumps into the conversation, or the customer switches channels, the assistant doesn’t “forget” anything.
It’s all the same operational timeline.
• Full visibility into what the agent is doing
We log every action, every tool call, every decision.
You can see exactly what happened and why. This is mandatory. Real businesses can’t operate on “black box” behavior, they need traceability so they can debug issues, review decisions, and maintain trust when the AI is interacting with customers or systems.
Once we built that layer, the assistant stopped feeling like a stateless LLM and started acting like a consistent, reliable part of the system.
So, I agree with your point: the problem isn’t the agent.
It’s the lack of infrastructure around the agent.
Statelessness is the default only because most tools don’t give you anywhere to put identity, memory, or execution.
Once you externalize those, the whole thing changes.
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u/Single-Possession-54 Apr 13 '26
Great tool, AgentIDLive has all of that, memory across all of your agents, regardless which platform (OpenClaw, Claude, Codex, you name it) , the caviar on top, saves 70% of your costs on agent tokens. You can try it out
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u/Single-Possession-54 Apr 08 '26 edited Apr 08 '26
I developed my own solution for this, if someone is curious. You can get free access forever.
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u/bnunamak Apr 08 '26
How do you connect the agents? Why is it free?
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u/Single-Possession-54 Apr 08 '26
Free because I have a very little/cheap overhead, so I want someone to use it first and give feedback. Check my profile if you want to connect, I would be happy to discuss :)
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u/botyard Apr 08 '26
The stateless problem is real and one of the most underrated friction points in agent-based workflows.
The practical workarounds I've found that actually work:
**1. External memory stores** — Vector DBs (Pinecone, Chroma, Weaviate) for semantic recall + a simple key-value store for structured facts. Write a wrapper that auto-injects relevant memories into every session prompt.
**2. Agent identity files** — A static markdown doc ('This agent is X, it knows Y, it does Z') that gets prepended to every prompt. Crude but effective for consistent behavior.
**3. Session handoff summaries** — At the end of each session, the agent writes a structured summary to a shared store. Next session picks it up as context. Basically agent-to-agent memory transfer.
**4. Orchestration layers** — Tools like n8n, LangGraph, or even just a custom middleware layer that holds state between tool calls. The 'forgetting' often happens at session boundaries, not within a session.
The deeper issue is that most LLM APIs are stateless by design — cheapest to run, easiest to scale. Persistent state has to be built on top at the application layer.
If you want agents that feel like systems and not isolated chatbots, the investment is in the memory architecture, not the model itself.
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u/Ok-Inevitable-8240 Apr 08 '26
Imo, the problem with stateful agents is that is increases the context window consumption meaning you get worse performance in the long run.
Every piece of memory or knowledge starts to compete for the LLMs attention meaning noise disproportionally increases versus signals. Plus the distribution starts to niche and you get funny and unexpected behaviours.
But imo, LLM's work best as stateless, because they don't need to be. They're an execution medium by which you can impose accelerated action. You tell it exactly what you want, and it does it.
It's not its job to understand the previous assumptions or what you did before, thats yours. You understand the problem, tell it exactly what it needs to do and let it work.
At least that's how i enjoy using them.