Imagine if your digital assistant could not only answer questions, but also submit expense reports, book travel, and resolve IT issues—by securely connecting to enterprise systems, making real-time decisions, and even processing voice or image inputs. This is the promise of modern AI agents: they blend advanced language understanding with the ability to act, orchestrate, and interact across modalities, evolving from simple chatbots into true digital coworkers.
Traditional foundation models—like Claude, Titan, or Llama—excel at generating text, summarizing information, or answering questions. However, on their own, they’re like expert advisors without hands: they can suggest, but not do. Agents bridge this gap by empowering language models to use tools—such as APIs, databases, or workflow engines—so the AI can take real actions, not just offer advice. As of 2025, leading Bedrock agents can also orchestrate workflows that span text, vision, and speech, enabling richer enterprise automation scenarios.
Why is this crucial for enterprise automation? Most business processes are not single-step Q&A exchanges. Take employee onboarding: it requires collecting documents, provisioning accounts, and scheduling training—steps that span multiple systems and may involve both text and document/image processing. Agents can reason through each stage, decide what to do next, and call the right tools to complete the workflow, handling exceptions and escalating as needed.
Amazon Bedrock’s agent framework brings this orchestration to life. Let’s break down what makes Bedrock agents powerful and production-ready in 2025:
Key Capabilities of Bedrock Agents (2025):
Let’s make this concrete. Suppose you want an agent to submit an expense report for a user. First, you define the available action using an OpenAPI schema—a machine-readable contract that describes how the agent can interact with an external API. In Bedrock, this schema defines an action group. Note: When using OpenAPI with Bedrock, ensure that any JSON examples use double quotes for all strings and follow standard JSON conventions.
# Action group: Expense Report Submissionpaths: /submitExpenseReport: post: summary: Submit a new expense report operationId: submitExpenseReport requestBody: required: true content: application/json: schema: type: object properties: employeeId: type: string # Employee submitting the report amount: type: number # Expense amount category: type: string # Expense category (e.g., Conferences) required: [employeeId, amount, category] responses: '200': description: Expense report submitted content: application/json: schema: type: object properties: reportId: type: string # Unique ID for the report status: type: string # Submission status
This schema tells Bedrock exactly what data the agent can send and receive. For example, when a user says, “Submit my travel expense for $500 under ‘Conferences’,” the agent extracts the details, fills the required parameters, and calls the correct API—automating a task that usually involves multiple manual steps. For endpoints that require JSON payloads, always use valid JSON formatting with double quotes.
As workflows get more complex—say, a missing receipt or an expense exceeding policy—the agent can reason about exceptions, ask follow-up questions, or escalate to a human reviewer. With multimodal support, the agent could even process an uploaded receipt image or respond with synthesized speech if required. This is where agents move far beyond chatbots, handling routine cases, surfacing only the exceptions, and orchestrating across modalities.
Let’s recap why agents matter for enterprise AI in 2025: