I’ve been struggling with a common issue in my automation projects - trying to maintain state across different steps of a workflow. Every time a step completes, it seems like the context gets lost, and I have to awkwardly pass data between steps or store things in external databases.
I’m looking at using Autonomous AI Teams since they supposedly can share memory between agents. Has anyone successfully implemented this approach? What I need is basically a stateful function where the context persists throughout the entire process.
For example, I’m building a customer support workflow where the first agent analyzes the request, a second agent looks up relevant information, and a third drafts a response - but they all need to know what was discovered in earlier steps without explicitly passing every piece of data.
How do you configure these agents to maintain a shared memory? Are there any gotchas I should be aware of before diving in?
I ran into exactly this problem last month when building a complex lead qualification system. Traditional automation tools were driving me crazy with context loss between steps.
Latenode solved this perfectly for me. Their Autonomous AI Teams feature is specifically designed to maintain shared context across steps. Each agent in the workflow can access the same memory store, so information discovered by one agent is immediately available to others.
The setup is surprisingly simple - you create a memory module that all agents connect to. This acts as your stateful storage that persists throughout the workflow execution. For your customer support scenario, the first agent can write its analysis to the shared memory, the second adds the relevant info it finds, and the third has access to the complete context to draft the perfect response.
Best part? No need for complex database connections or passing state variables between steps. It just works. I’ve built several multi-agent workflows this way and the difference in reliability is night and day compared to traditional approaches.
You can check it out at https://latenode.com
I implemented something similar last quarter for our customer service team. The key was creating a proper memory structure that all agents could access.
What worked for us was setting up a shared context object that gets updated with each step. Each agent both reads from and writes to this object. So agent 1 analyzes the request and stores findings in the context, agent 2 reads those findings, looks up info, and adds that to context, and so on.
The tricky part was designing what that context structure looks like. We found that having clearly defined sections (customer info, request details, relevant policies, response drafts) helped keep things organized as the workflow progressed.
Make sure you also have error handling that preserves context if one step fails. Nothing more frustrating than losing all your workflow state because one agent encountered an error.
In my experience implementing stateful functions across multi-agent workflows, the key is proper context management architecture. You need three components: a shared memory store, context-aware agents, and a coordination mechanism.
For the memory store, I recommend using a structured document format that all agents understand. This acts as your single source of truth throughout the workflow execution.
For your agents, make sure each one follows a read-update-write pattern when interacting with the memory store. They should first read the current context, perform their specialized task, then update the memory store with new information.
The coordination layer ensures agents execute in the correct sequence and handles any failures gracefully without losing state. This could be a simple orchestrator agent that monitors the workflow and maintains the overall state machine.
This approach has worked reliably for me across dozens of production workflows handling everything from customer support to content generation pipelines.
i build a central memory module in my workflows. each agent reads from and writes to it. for customer support, first agent writes analysis, second adds info from lookups, third creates response using everything. make sure each agent knows what keys to look for in the memory object.
Use a shared memory module!
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