How To Build Your First Agentic AI App with AWS Bedrock

In the fast-moving AI landscape of 2025, Agentic AI is emerging as the next major advancement. It moves us beyond basic chatbots and one-off automations into systems that can reason, plan, and act autonomously to achieve complex goals with minimal human oversight.

At the center of this shift is AWS Bedrock, Amazon’s fully managed, serverless platform for generative AI. With its latest updates — including the AgentCore runtime — Bedrock is becoming a preferred framework for enterprises that want to deploy secure, scalable, and intelligent AI agents.

By Q2 2024, more than 10,000 organizations had adopted AWS Bedrock for AI projects — a 300% year-over-year increase (Axios AI+).

What Is Agentic AI?

Agentic AI represents a new class of intelligent systems designed to operate as independent agents. Unlike traditional bots that simply respond to queries, agentic AI can:

• Perceive context from data and interactions
• Reason about goals, constraints, and next steps
• Act by triggering workflows and APIs automatically
• Adapt as new information emerges

In short: Agentic AI doesn’t just answer questions — it executes tasks end-to-end.

Why It Matters in 2025

Modern businesses face an explosion of complexity in customer support, operations, and knowledge management. Rules-based systems and static knowledge bases no longer scale.

Agentic AI enables:

• Personalized customer interactions that adjust in real time
• Automated onboarding and internal support that reduces manual workload
• Intelligent IT and supply chain operations with proactive monitoring
• Faster decision-making by contextualizing data across multiple systems

A 2024 McKinsey report found that organizations using agentic AI in finance, logistics, and compliance achieved 30–70% gains in operational efficiency.

Real-World Adoption: Who’s Leading?

• JPMorgan Chase → Running 1,500+ use cases on AWS’s generative AI stack, including trade simulations and client advisory bots.
• Siemens → Reduced factory downtime by 25% with autonomous agents addressing sensor anomalies.
• Boomi → Built 12 internal agents across support, finance, and legal in under four months using Bedrock’s AgentCore.
• AWS AI Centers → $100M investment to accelerate enterprise adoption of agentic AI.

Examples of Agentic AI in Action

• Healthcare Assistant → Evaluates symptoms, retrieves medical records, checks drug interactions, and suggests next steps.
• Financial Advisor → Reviews portfolios, analyzes markets, and generates investment plans tailored to user preferences.
• DevOps Agent → Oversees cloud infrastructure, forecasts anomalies, triggers repair scripts, and updates stakeholders automatically.

Why AWS Bedrock Is Ideal for Agentic Apps

AWS Bedrock brings unique advantages for building production-grade agents:

• Diverse Foundation Models (Single API)
  Access AWS Titan, Anthropic Claude, Cohere, Mistral, DeepSeek-R1, and more — all via one API with minimal code changes.
• Managed Retrieval & Fine-Tuning
  Fine-tune models privately and set up RAG (retrieval-augmented generation) with Knowledge Bases powered by OpenSearch, Pinecone, Redis, or Aurora.
• Agent Architecture
  Configure action groups (Lambda, APIs) and knowledge bases so agents can converse, plan, and execute workflows.
• Guardrails & Evaluation
  Built-in safety filters, audit logs, and evaluation tools help maintain compliance and trust.
• Enterprise-Scale with AgentCore
  Long-running workflows (up to 8 hours), cross-agent protocols, governance features, and monitoring for production environments.
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Step-by-Step: Building Your First Agentic AI App with Bedrock

Step 1: Access & Environment Setup

• Enable AWS Bedrock from the AWS Console
• Set up IAM roles and permissions
• Choose Bedrock Studio (GUI) or Bedrock API (code)
• Activate services like S3 (data), Lambda (actions), OpenSearch/Redis (knowledge base)
  Pro Tip: Keep dev, test, and prod environments separate.

Step 2: Choose the Right Foundation Model

Options include Claude 3.5 Sonnet, Cohere Command R+, Llama 3, Mistral, and Amazon Titan.
Pro Tip: Test in the Bedrock playground for cost/latency trade-offs.

Step 3: Build a Knowledge Base (RAG Setup)

• Upload documents to Amazon S3
• Vectorize content with embeddings
• Connect to OpenSearch Serverless, Pinecone, or Redis
• Test retrieval quality
   Use Case: A legal assistant answering compliance questions from internal PDFs.

Step 4: Define Action Groups

• Use OpenAPI or JSON schema to define inputs/outputs
• Link to Lambda functions or external APIs
• Add natural-language descriptions for tool selection
  Example: Travel planner actions → getFlightOptions(), bookHotel(), generateItinerary()

Step 5: Configure & Deploy the Agent

• In the Bedrock console, assign:
  • Foundation model
  • Knowledge base
  • Action groups
  • Guardrails + memory settings
• Define orchestration (e.g., ReAct for reasoning or Toolformer-style for API calls)

Step 6: Test, Monitor, & Refine

• Simulate real queries
• Monitor error rates, API failures, latency, resolution times
• Collect feedback → retrain with failed cases
  Use A/B testing to validate before scaling.

Step 7: Scale with AgentCore Runtime

• Run workflows up to 8 hours
• Enable cross-agent communication
• Ensure session isolation for users
• Set up audit logs and governance policies
  Architecture tip: Combine Kafka + Bedrock + AgentCore for real-time event-driven agents.

In a Nutshell

AWS Bedrock gives enterprises the foundation to move from experiments to true agentic AI at scale. With Bedrock and AgentCore, organizations can:

• Improve decision-making
• Automate workflows
• Personalize customer and employee interactions
• Deploy secure, compliant, and production-ready AI systems

Whether you’re a startup exploring copilots or a large enterprise deploying AI at scale, Bedrock offers a secure and flexible path into the future of Agentic AI.