Our team has successfully deployed a basic Retrieval-Augmented Generation (RAG) system, but it's hitting limits with complex tasks. We're looking to transition to "Agentic AI" where multiple agents collaborate on workflows. Does anyone have experience managing the hand-off between specialized agents without losing context or increasing latency significantly?
3 answers
The "Agentic Leap" requires moving away from linear chains to an orchestration layer. In our 2024 deployment, we utilized a "Manager Agent" pattern where a central LLM routes tasks to specialized "Worker Agents" (e.g., a SQL Agent, a Document Agent, and a Coder Agent). The biggest challenge isn't the logic—it's the state management. We found that using a centralized vector database for "Short-term Memory" allows agents to share the conversation history without bloating the prompt token count. This reduced our hallucination rate by 30% while keeping our API costs manageable during high-concurrency periods.
Are you currently using an orchestration framework like LangGraph or AutoGen to handle these cycles, or are you trying to build a custom routing logic from scratch?
Start small. Don't try to build five agents at once. Perfect the hand-off between two agents first, ensure the data schema is consistent, and then scale up the complexity.
Michael is spot on. Schema consistency is the silent killer of agentic workflows. If Agent A outputs JSON and Agent B expects Markdown, the whole system collapses.
We are currently testing LangGraph for its stateful management. However, we're finding that the overhead of passing the entire state between agents is starting to cause 5-second delays in our UI. David, do you think moving the routing logic to a smaller, faster model like Llama 3-8B while keeping the heavy lifting for GPT-4o would solve the latency issue without sacrificing the accuracy of the task delegation?