Curriculum Overview: The Role of Agents in Multi-Step Tasks (Amazon Bedrock & Agentic AI)
Describe the role of agents in multi-step tasks (for example, Amazon Bedrock Agents, agentic AI, model context protocol)
Curriculum Overview: The Role of Agents in Multi-Step Tasks
Welcome to the curriculum overview for Agentic AI and Amazon Bedrock Agents. This learning path is designed to align with the AWS Certified AI Practitioner (AIF-C01) exam guide, specifically focusing on how autonomous agents orchestrate complex, multi-step workflows using Foundation Models (FMs), Knowledge Bases, and APIs.
Prerequisites
Before diving into this curriculum, learners must have a foundational understanding of Generative AI concepts and basic cloud computing architectures.
- Generative AI Fundamentals: You should understand what Foundation Models (FMs) are, how text is converted into tokens and embeddings, and basic prompt engineering concepts.
- Cloud Architecture Basics: Familiarity with AWS core services, particularly Amazon S3 (for storing data) and AWS Lambda (for executing backend logic).
- API & Integration Concepts: A working knowledge of how applications communicate via APIs (e.g., RESTful services and OpenAPI schemas).
- Data Sources: A basic grasp of vector databases and Retrieval-Augmented Generation (RAG).
[!IMPORTANT] If you are unfamiliar with RAG, we recommend reviewing Amazon Bedrock Knowledge Bases prior to starting this curriculum, as agents heavily rely on RAG for contextual accuracy.
Module Breakdown
This curriculum is structured to take you from foundational agent concepts to designing advanced, secure, multi-agent workflows.
| Module | Title | Core Focus | Difficulty | Estimated Time |
|---|---|---|---|---|
| Module 1 | Introduction to Agentic AI | Defining autonomous agents, understanding their role in automating multi-step tasks, and model context protocol. | Beginner | 2 Hours |
| Module 2 | Core Components of Bedrock Agents | Foundation Models, Instructions, Action Groups (APIs/Lambda), and Schemas. | Intermediate | 3 Hours |
| Module 3 | Knowledge Bases & Context Management | Integrating Bedrock Knowledge Bases (RAG) to ground agent responses in proprietary enterprise data. | Intermediate | 2.5 Hours |
| Module 4 | Multi-Agent Collaboration | Designing specialized agents (e.g., Supervisor, Routing, and Research agents) that collaborate to complete complex goals. | Advanced | 3.5 Hours |
| Module 5 | Evaluation, Pricing, & Guardrails | Assessing agent performance, calculating provisioned throughput vs. on-demand pricing, and enforcing AI safety guardrails. | Advanced | 2 Hours |
Learning Objectives per Module
Module 1: Introduction to Agentic AI
- Define the role of intelligent autonomous agents in multi-step enterprise tasks.
- Explain how Agentic AI reduces human error and automates time-consuming processes like data aggregation and reporting.
- Identify how agents orchestrate actions dynamically without relying on static, hard-coded logic.
Module 2: Core Components of Bedrock Agents
- Select the appropriate Foundation Model (FM) for agent orchestration based on cost, latency, and reasoning capabilities.
- Configure explicit, natural-language instructions to define the agent's purpose.
- Design Action Groups using OpenAPI schemas and AWS Lambda to enable agents to interact with external enterprise systems (e.g., checking inventory or making reservations).
Module 3: Knowledge Bases & Context Management
- Integrate an agent with Amazon Bedrock Knowledge Bases to fetch up-to-date company data.
- Describe the workflow of securely querying a vector database to enrich prompts dynamically.
Module 4: Multi-Agent Collaboration
- Design a multi-agent system where a central decision-maker (Supervisor Agent) delegates sub-tasks to specialized expert agents.
- Map out interaction logs to track how multiple agents communicate, synthesize data, and reach a consensus.
Module 5: Evaluation, Pricing, & Guardrails
- Evaluate cost tradeoffs in Amazon Bedrock (e.g., On-demand token pricing vs. Provisioned Throughput for varied agent workloads).
- Implement guardrails to prevent agentic systems from executing unauthorized actions or hallucinating data in high-stakes environments.
Visual Architectures
The Bedrock Agent Workflow
Understanding how a single agent processes a user request is crucial. The agent dynamically decides whether it needs to query a knowledge base or execute an action via an API.
Multi-Agent Collaboration Architecture
For complex enterprise applications, a single agent may not suffice. A Multi-Agent architecture delegates specific roles to specialized models, creating a comprehensive "team" of AI assistants.
Success Metrics
How will you know you have mastered the material in this curriculum? You should be able to:
- Conceptual Mastery: Pass AIF-C01 practice exam questions related to Task Statement 3.1: Describe the role of agents in multi-step tasks.
- Architectural Design: Diagram a complete agentic workflow from user input to Lambda execution without referencing documentation.
- Practical Deployment: Build an interactive Amazon Bedrock Agent in the AWS console that correctly calls an OpenAPI schema to retrieve mock data.
- Cost Estimation: Calculate the projected costs of running a multi-agent system on Bedrock using on-demand token pricing vs. provisioned throughput.
Real-World Application
Agentic AI is moving enterprises beyond simple Q&A chatbots into the realm of autonomous task execution.
- Retail & Customer Support: Consider an automotive parts retailer. Instead of a human agent looking up manuals, an Amazon Bedrock agent can parse the query "What wiper blades fit a 2021 Honda CR-V?", call the inventory database API, check compatibility, and process an order return automatically.
- Financial Services: In finance, autonomous agents seamlessly handle complex workflows like monitoring real-time data streams, detecting anomalies, and triggering immediate corrective actions. A Financial Analyst Agent can aggregate outputs from a Stock Data Agent and a News Researcher Agent to generate a holistic buy/sell recommendation instantly, minimizing financial risk and eliminating manual oversight bottlenecks.
[!TIP] The core value proposition of Agentic AI is freeing human capital. By managing repetitive, multi-step tasks, agents allow employees to focus on strategic, high-value activities rather than manual data routing.