Building agentic workflows with SageMaker AI and Bedrock AgentCore

The integration of specialized AI agents with diverse model capabilities presents a significant opportunity to streamline complex, multi-step tasks across a business. This piece from AWS Machine Learning outlines a method for constructing "agentic workflows" by combining OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime. It demonstrates how to orchestrate multiple AI agents, each leveraging the most appropriate model for its specific sub-task, and importantly, shows how to gain granular token-level observability from SageMaker endpoints, which is not typically available by default. This technical pattern fundamentally affects how organizations can automate sophisticated processes, moving beyond single-model applications to a distributed intelligence approach. For a logistics startup in Chicago, optimizing delivery routes and predicting supply chain disruptions could involve one agent analyzing real-time traffic data with a specialized geo-spatial model, while another uses a predictive analytics model to forecast demand based on historical sales and weather patterns, all feeding into a central planning agent. An independent SaaS founder building a customer support platform might deploy a multi-agent system where one agent classifies incoming queries using a fine-tuned sentiment model, a second retrieves relevant knowledge base articles with an embedding model, and a third crafts a personalized response using a powerful large language model, significantly reducing resolution times. Even an internal IT team at a mid-size manufacturing firm in Detroit could use this to automate incident response: an agent identifies system anomalies, another diagnoses the root cause by cross-referencing logs, and a third generates a preliminary remediation plan, all without human intervention in the initial stages. To begin experimenting with this concept, select a repetitive, multi-stage task within your current workflow that involves distinct types of analysis or decision-making. Map out the individual steps and identify at least two different AI model types that might be best suited to each stage. Try to connect these steps using a basic scripting framework, even if it's just passing outputs manually for now, to conceptualize how specialized agents could eventually handle the handoffs automatically.