Amazon Bedrock AgentCore just shipped a managed agent harness that lets you go from zero to a running LangGraph agent in three API calls. This tutorial walks you through it — from setup to first real request.

Time required: ~10 minutes
Prerequisites: AWS account, Python 3.10+, AWS CLI configured
Frameworks supported: LangGraph, CrewAI, LlamaIndex, Strands Agents

Why AgentCore’s Managed Harness Changes the Game

Before AgentCore’s new features, getting an agent into a production-grade environment meant wiring up:

  • Compute to host the agent
  • A sandboxed execution environment
  • Secure tool connections
  • Persistent memory/storage
  • Authentication and error recovery

That’s days of infrastructure work before your agent handles a single real request. AgentCore’s managed harness replaces all of that with a configuration declaration. You define what your agent does; AgentCore provides the runtime.

Step 1: Install the SDK and Configure AWS

pip install boto3 langchain-aws langgraph

# Verify your AWS credentials
aws sts get-caller-identity

Make sure your IAM role has permissions for bedrock:InvokeModel and bedrock-agent:*. If you’re working locally, your default profile credentials will work. In production, use an IAM role attached to your compute.

Step 2: Define Your Agent in Three API Calls

The new managed harness API is built around three operations: create, invoke, and list. Here’s a minimal working agent:

import boto3
import json

# Initialize the AgentCore client
client = boto3.client("bedrock-agent", region_name="us-east-1")

# Step 1: Create the agent definition
agent = client.create_agent(
    agentName="my-first-langgraph-agent",
    agentResourceRoleArn="arn:aws:iam::YOUR_ACCOUNT:role/BedrockAgentRole",
    foundationModel="anthropic.claude-3-5-sonnet-20241022-v2:0",
    instruction="You are a helpful assistant. Use available tools to answer questions accurately.",
)

agent_id = agent["agent"]["agentId"]
print(f"Agent created: {agent_id}")

# Step 2: Prepare (deploy) the agent
client.prepare_agent(agentId=agent_id)

# Step 3: Invoke
runtime_client = boto3.client("bedrock-agent-runtime", region_name="us-east-1")

response = runtime_client.invoke_agent(
    agentId=agent_id,
    agentAliasId="TSTALIASID",  # Use TSTALIASID for the draft version
    sessionId="session-001",
    inputText="What are the key features of Amazon Bedrock AgentCore?"
)

# Stream the response
for event in response["completion"]:
    if "chunk" in event:
        print(event["chunk"]["bytes"].decode("utf-8"), end="")

That’s it. AgentCore handles compute provisioning, the execution sandbox, memory initialization, and the request routing.

Step 3: Add LangGraph as Your Orchestration Framework

AgentCore integrates natively with LangGraph for teams that want explicit graph-based agent orchestration. Here’s how to wrap a LangGraph graph as an AgentCore-compatible agent:

from langchain_aws import ChatBedrock
from langgraph.graph import StateGraph, END
from typing import TypedDict, List

# Define your state
class AgentState(TypedDict):
    messages: List[dict]
    next_step: str

# Initialize the LLM via Bedrock
llm = ChatBedrock(
    model_id="anthropic.claude-3-5-sonnet-20241022-v2:0",
    region_name="us-east-1"
)

# Build a simple two-node graph
def reasoning_node(state: AgentState) -> AgentState:
    """Think about what to do next."""
    response = llm.invoke(state["messages"])
    return {
        "messages": state["messages"] + [{"role": "assistant", "content": response.content}],
        "next_step": "done"
    }

def should_continue(state: AgentState) -> str:
    return state["next_step"]

# Compile the graph
graph = StateGraph(AgentState)
graph.add_node("reasoning", reasoning_node)
graph.add_conditional_edges("reasoning", should_continue, {"done": END})
graph.set_entry_point("reasoning")
agent_graph = graph.compile()

# Run it
result = agent_graph.invoke({
    "messages": [{"role": "user", "content": "Explain the new AgentCore managed harness in one paragraph."}],
    "next_step": "continue"
})

print(result["messages"][-1]["content"])

When you deploy this through AgentCore’s managed harness, you get persistent session memory, sandboxed tool execution, and production compute without additional configuration.

Step 4: Add a Tool

Real agents need tools. Here’s how to register a web search tool with your AgentCore agent:

# Define the tool schema
tool_schema = {
    "name": "web_search",
    "description": "Search the web for current information",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query"
            }
        },
        "required": ["query"]
    }
}

# Register the action group with your agent
client.create_agent_action_group(
    agentId=agent_id,
    agentVersion="DRAFT",
    actionGroupName="web-tools",
    actionGroupExecutor={
        "lambda": "arn:aws:lambda:us-east-1:YOUR_ACCOUNT:function:your-search-lambda"
    },
    apiSchema={
        "payload": json.dumps({
            "openapi": "3.0.0",
            "info": {"title": "Web Tools", "version": "1.0"},
            "paths": {
                "/search": {
                    "post": {
                        "operationId": "web_search",
                        "requestBody": {
                            "content": {
                                "application/json": {
                                    "schema": {
                                        "type": "object",
                                        "properties": {
                                            "query": {"type": "string"}
                                        }
                                    }
                                }
                            }
                        }
                    }
                }
            }
        })
    }
)

What You Get From AgentCore by Default

Once your agent is running through the managed harness, you automatically have:

  • Persistent session memory — conversation context survives across requests in the same session
  • Sandboxed code execution — tool calls run in an isolated environment
  • Managed compute — no EC2, ECS, or Lambda cold starts to configure
  • Identity and auth — IAM-based authentication built in
  • Error recovery — the harness handles retries and failure states

Next Steps

  • Add multi-turn memory with Bedrock’s built-in session storage
  • Connect to a knowledge base via Bedrock Knowledge Bases for RAG workflows
  • Scale to multiple agents with AgentCore’s cross-agent communication features
  • Explore CrewAI or LlamaIndex integration if your use case fits those frameworks better

The AgentCore documentation has working examples for each framework: AWS Bedrock AgentCore Docs

Sources

  1. Amazon Web Services Machine Learning Blog — New Features in Amazon Bedrock AgentCore
  2. LangGraph Documentation
  3. AWS Bedrock AgentCore Documentation

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260422-2000

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