Jason Bloomberg writing for DZone
Explore the unique state management challenges posed by agentic AI, and see why traditional cloud-native approaches fall short as AI agents evolve to learn and adapt over time.
When you have a conversation with a chatbot, you want it to remember previous interactions within that conversation. That’s what it means to have a conversation, after all.
When you use generative AI (genAI) to perform some analysis task beyond a single response to a prompt, you want it to retain the context of earlier prompts within that task.
When a company wants AI to automate a workflow — a sequence of steps over time, with human input along the way — you want the AI to keep track of where each user is along their instance of the workflow.
These examples are all situations where we expect our AI to maintain state information — some persisted data that keeps track of interactions or automated tasks over time.
Now that agentic AI is here, however, these examples of state management don’t go far enough.
The missing piece: we want AI to learn. We want our agents to get smarter over time.
Suddenly, all our traditional approaches to managing the state of interactions in a distributed computing environment fall short.
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