Amazon Bedrock serves as AWS’s central hub for generative AI, and with the addition of agentic capabilities, it has become a playground for creating powerful AI agents in the cloud. Using Agents for Bedrock, a developer can define “skills” or APIs that an agent is allowed to use – for instance, an order tracking API, an email API, and an AWS Lambda function. The agent is given an instruction like “Assist the customer in resolving their order issue.” Backed by Bedrock’s language models, the agent will determine a sequence of actions: perhaps query the order database (via the provided API) to find status, then compose a response to the customer, and even initiate a refund process by calling a Lambda. It essentially merges LLM intelligence with the power of AWS’s cloud services. This design opens endless possibilities – companies can create support bots, workflow automation bots, research assistants, all underpinned by Bedrock’s stable infrastructure and choice of high-quality models. An advantage of Bedrock Agents is security and control: since developers explicitly configure what tools/data the agent can access, it operates within guardrails suitable for enterprise settings. AWS has highlighted examples like an agent that automates data analytics reporting (fetching data from AWS data lakes, running summary stats, then emailing a report), and another that handles IT support requests by diagnosing issues and fixing infrastructure via AWS service calls. Importantly, Bedrock’s serverless scaling ensures these agents can run at enterprise scale. By offering this on AWS, Amazon is enabling customers to bring custom autonomous AI into their own cloud environment, tightly integrating with their existing AWS-driven workflows – effectively making AI agents a natural extension of one’s cloud architecture (36 Top Agentic AI Vendors in 2024 + 3 Free Options) (36 Top Agentic AI Vendors in 2024 + 3 Free Options).