We are exploring the new Bedrock AgentCore upgrades for our customer service workflow. I'm specifically looking for advice on managing multi-step reasoning and long-term memory for autonomous agents. How do you handle the orchestration of multiple agents without running into massive latency, and what are the best practices for setting up guardrails to prevent hallucinations in a live environment?
3 answers
Scaling autonomous agents requires a decoupled architecture. With the 2024 Bedrock AgentCore updates, the key is leveraging "Session Persistence" for long-term memory. You should utilize DynamoDB to store the conversation context and state, then pass that context into the AgentCore API. To manage latency, avoid serial execution; instead, use an asynchronous pattern with AWS Step Functions to orchestrate different agent tasks. For guardrails, don't rely solely on the built-in filters—implement a "Verification Agent" that scans the final output against your internal knowledge base to ensure 100% accuracy before the customer sees the response.
Are you planning to use the provided foundation models like Claude 3.5 or Titan, or are you looking to integrate your own fine-tuned models into the AgentCore framework?
The most important part of Bedrock Agents is the Action Group definition. If your Lambda functions aren't optimized, the agent will time out while waiting for a tool response.
Exactly, Jennifer! I've found that using Provisioned Concurrency for the underlying Lambda Action Groups is the only way to keep the agent's "thinking" time under three seconds.
We are currently using Claude 3.5 Sonnet because of its superior reasoning capabilities. However, we are finding that the "cold start" for the initial agent response is a bit sluggish. I’m curious if switching to the newly released Amazon Nova models would provide a faster inference time without sacrificing the quality of the multi-step planning we need for complex tickets.