Building Agentic AI on AWS: From Bedrock Agents to Multi-Agent Orchestration with AgentCore

2026 is the year AI moved from "answer questions" to "take actions." Agentic AI — systems that autonomously plan, reason, use tools, and execute multi-step tasks — has become the dominant pattern for building intelligent applications on AWS.

This post covers the full agentic AI stack on AWS: from single-agent basics to multi-agent orchestration, the infrastructure that runs them, and the guardrails that keep them safe in production.

Traditional AI: User asks question → Model generates answer → Done.

Agentic AI: User states goal → Agent plans steps → Agent calls tools → Agent evaluates results → Agent iterates → Goal achieved.

The difference is autonomy. An agent decides what to do, executes actions, and self-corrects — without human intervention at each step.

┌─────────────────────────────────────────────────────────────┐
│                    AGENTIC AI LOOP                            │
│                                                              │
│   User Goal → Plan → Act → Observe → Reason → Act → Done   │
│                  ↑                               │           │
│                  └───────── iterate ──────────────┘           │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│  APPLICATION LAYER                                               │
│  Amazon Q (Business & Developer) | Custom agents via Bedrock    │
├─────────────────────────────────────────────────────────────────┤
│  AGENT FRAMEWORKS                                                │
│  Bedrock Agents | Strands Agents SDK | LangGraph on AgentCore   │
├─────────────────────────────────────────────────────────────────┤
│  AGENT INFRASTRUCTURE (AgentCore)                                │
│  Runtime | Memory | Identity | Observability | Code Interpreter │
├─────────────────────────────────────────────────────────────────┤
│  TOOLS & KNOWLEDGE                                               │
│  AgentCore Gateway (MCP) | Knowledge Bases (RAG) | Action Groups│
├─────────────────────────────────────────────────────────────────┤
│  SAFETY & GOVERNANCE                                             │
│  Guardrails | IAM | CloudTrail | Model Evaluation               │
├─────────────────────────────────────────────────────────────────┤
│  FOUNDATION MODELS                                               │
│  Claude | Nova | Llama | Mistral | DeepSeek (via Bedrock)       │
└─────────────────────────────────────────────────────────────────┘

Bedrock Agents is the fully managed way to build AI agents. You define the agent's instructions, connect tools and knowledge, and Bedrock handles the orchestration loop (ReAct-style reasoning).

Choosing the model: Claude Sonnet or Nova Pro for complex reasoning. Haiku or Nova Micro for simple routing agents.

Instruction design: Be specific about the agent's role, what it should NOT do, and how to handle ambiguity. Vague instructions lead to unpredictable behavior.

Tool design: Each tool should do ONE thing well. Name them clearly (the model uses the name and description to decide when to call them). Include input/output schemas.

{
  "actionGroupName": "OrderManagement",
  "description": "Manages customer orders - lookup, modify, cancel",
  "apiSchema": {
    "payload": "openapi-schema.json"
  },
  "actionGroupExecutor": {
    "lambda": "arn:aws:lambda:us-east-1:123456789:function:order-api"
  }
}

For complex problems, a single agent isn't enough. Multi-agent collaboration lets specialized agents work together:

                    ┌──────────────────┐
                    │  Supervisor Agent │
     User ────────→│  (Routes tasks)  │
                    └────────┬─────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
              ▼              ▼              ▼
     ┌──────────────┐ ┌───────────┐ ┌──────────────┐
     │ Research Agent│ │ Code Agent│ │ Review Agent │
     │ (RAG + Web)  │ │ (CodeGen) │ │ (Validation) │
     └──────────────┘ └───────────┘ └──────────────┘

Rule of thumb: If one agent would need >10 tools or >3 distinct responsibilities, split into multiple agents.

AgentCore is the runtime infrastructure for deploying agents at scale. It provides the "boring but critical" capabilities agents need in production:

The Gateway is particularly powerful — it transforms your existing APIs into tools that any agent can discover and use via the Model Context Protocol (MCP):

This means agents don't need tools baked into their code. They discover capabilities dynamically — add a new API to Gateway, and all connected agents can immediately use it.

For teams wanting more control, AWS released Strands Agents SDK — an open-source Python framework for building agents that runs on AgentCore:

@tool

decorator, auto-generating schemas

from strands import Agent, tool
from strands.models.bedrock import BedrockModel

@tool
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"Weather in {city}: 22°C, sunny"

@tool
def create_ticket(title: str, priority: str) -> str:
    """Create a support ticket in the ticketing system."""
    return f"Created ticket: {title} (priority: {priority})"

agent = Agent(
    model=BedrockModel(model_id="anthropic.claude-sonnet-4-20250514"),
    tools=[get_weather, create_ticket],
    system_prompt="You are a helpful assistant that can check weather and create tickets."
)

response = agent("Check the weather in London and create a ticket if it's raining")

Without knowledge, agents hallucinate. Knowledge Bases provide RAG (Retrieval-Augmented Generation):

The latest option — fully managed RAG without provisioning anything:

Agents that take actions need safety boundaries. Bedrock Guardrails provides:

Guardrails attach to:

Key insight: Apply guardrails on BOTH input (what users send) AND output (what agents respond). Users can craft prompts to bypass instructions — guardrails are the defense layer.

Best for: Customer support, multi-domain queries.

One supervisor routes to specialist workers. Workers don't talk to each other.

Best for: Document processing, content creation.

Agent A → Agent B → Agent C. Each stage enriches output.

Best for: Research, data gathering from multiple sources.

Multiple agents work simultaneously, results aggregated.

Best for: High-stakes decisions, code review.

Generator agent produces output, critic agent evaluates quality, iterate until criteria met.

Before deploying agents to production:

The agentic AI space on AWS is evolving rapidly:

Building agentic AI on AWS in 2026:

The shift from "chatbot that answers" to "agent that acts" is the defining pattern of cloud AI in 2026. The infrastructure is ready — the question is what you build on it.

Alpesh Kumbhare is an AWS Architect at Atos, specializing in AWS infrastructure automation and cloud AI solutions. Connect on LinkedIn.