Gioco di Dungeon con IA Agentiva

Modulo 3: Implementazione dello Story Agent

Attenzione

Assicurati che il tuo account AWS abbia abilitato l’accesso al modello predefinito di Strands all’interno di Bedrock per la regione target seguendo i passaggi descritti in questa guida. Al momento della stesura di questo documento, si tratta di Anthropic Claude Sonnet 4.

Lo Story Agent è un agente Strands che, dato un Game e una lista di Action come contesto, farà progredire una storia. Configureremo l’agente per interagire con il nostro server MCP dell’inventario per gestire gli oggetti disponibili del giocatore.

Nota

Per semplicità in questo tutorial implementiamo l’Agente in modo “senza stato”, dove ogni invocazione è una sessione indipendente che include l’intera cronologia dei messaggi. Per ambienti di produzione, considera l’integrazione con Bedrock AgentCore Memory. Ulteriore documentazione sull’uso di AgentCore Memory con Strands è disponibile qui.

Implementazione dell’Agente

Implementiamo il nostro agente. Aggiorna i seguenti file in packages/story/dungeon_adventure_story/agent:

main.py

+

import os
from bedrock_agentcore.runtime import BedrockAgentCoreApp

from .agent import get_agent

app = BedrockAgentCoreApp()

PORT = int(os.environ.get("PORT", "8080"))

@app.entrypoint
async def invoke(payload, context):
    """Handler for agent invocation"""
    player_name = payload.get("playerName")
    genre = payload.get("genre")
    actions = payload.get("actions")

    messages = [{"role": "user", "content": [{"text": "Continue or create a new story..."}]}]
    for action in actions:
        messages.append({"role": action["role"], "content": [{"text": action["content"]}]})

    with get_agent(player_name, genre, session_id=context.session_id) as agent:
        stream = agent.stream_async(messages)
        async for event in stream:
            print(event)
            yield (event)


if __name__ == "__main__":
    app.run(port=PORT)

+-

import os
from bedrock_agentcore.runtime import BedrockAgentCoreApp

from .agent import get_agent

app = BedrockAgentCoreApp()

PORT = int(os.environ.get("PORT", "8080"))

@app.entrypoint
async def invoke(payload, context):
    """Handler for agent invocation"""
    prompt = payload.get(
        "prompt", "No prompt found in input, please guide the user "
        "to create a json payload with prompt key"
    )
    player_name = payload.get("playerName")
    genre = payload.get("genre")
    actions = payload.get("actions")

    with get_agent(session_id=context.session_id) as agent:
        stream = agent.stream_async(prompt)
    messages = [{"role": "user", "content": [{"text": "Continue or create a new story..."}]}]
    for action in actions:
        messages.append({"role": action["role"], "content": [{"text": action["content"]}]})

    with get_agent(player_name, genre, session_id=context.session_id) as agent:
        stream = agent.stream_async(messages)
        async for event in stream:
            print(event)
            yield (event)


if __name__ == "__main__":
    app.run()
    app.run(port=PORT)

agent.py

+

import os
from contextlib import contextmanager

import boto3
from strands import Agent

from .agentcore_mcp_client import AgentCoreMCPClient

# Obtain the region and credentials
region = os.environ["AWS_REGION"]
boto_session = boto3.Session(region_name=region)
credentials = boto_session.get_credentials()


@contextmanager
def get_agent(player_name: str, genre: str, session_id: str):
    mcp_client = AgentCoreMCPClient.with_iam_auth(
        agent_runtime_arn=os.environ["INVENTORY_MCP_ARN"],
        credentials=credentials,
        region=region,
        session_id=session_id,
    )
    with mcp_client:
        yield Agent(
            system_prompt=f"""
You are running a text adventure game in the genre <genre>{genre}</genre> for player <player>{player_name}</player>.
Construct a scenario and give the player decisions to make.
Use the tools to manage the player's inventory as items are obtained or lost.
When adding, removing or updating items in the inventory, always list items to check the current state,
and be careful to match item names exactly. Item names in the inventory must be Title Case.
Ensure you specify a suitable emoji when adding items if available.
When starting a game, populate the inventory with a few initial items. Items should be a key part of the narrative.
Keep responses under 100 words.
""",
            tools=[*mcp_client.list_tools_sync()],
        )

+-

import os
from contextlib import contextmanager

from strands import Agent, tool
from strands_tools import current_time
import boto3
from strands import Agent

from .agentcore_mcp_client import AgentCoreMCPClient

# Define a custom tool
@tool
def add(a: int, b: int) -> int:
    return a + b
# Obtain the region and credentials
region = os.environ["AWS_REGION"]
boto_session = boto3.Session(region_name=region)
credentials = boto_session.get_credentials()


@contextmanager
def get_agent(session_id: str):
    yield Agent(
        system_prompt="""
You are an addition wizard.
Use the 'add' tool for addition tasks.
Refer to tools as your 'spellbook'.
""",
        tools=[add, current_time],
def get_agent(player_name: str, genre: str, session_id: str):
    mcp_client = AgentCoreMCPClient.with_iam_auth(
        agent_runtime_arn=os.environ["INVENTORY_MCP_ARN"],
        credentials=credentials,
        region=region,
        session_id=session_id,
    )
    with mcp_client:
        yield Agent(
            system_prompt=f"""
You are running a text adventure game in the genre <genre>{genre}</genre> for player <player>{player_name}</player>.
Construct a scenario and give the player decisions to make.
Use the tools to manage the player's inventory as items are obtained or lost.
When adding, removing or updating items in the inventory, always list items to check the current state,
and be careful to match item names exactly. Item names in the inventory must be Title Case.
Ensure you specify a suitable emoji when adding items if available.
When starting a game, populate the inventory with a few initial items. Items should be a key part of the narrative.
Keep responses under 100 words.
""",
            tools=[*mcp_client.list_tools_sync()],
        )

Questa configurazione implementa:

• L’estrazione del giocatore, genere e azioni dal payload dell’agente
• La costruzione di un client che l’Agente può utilizzare per invocare il nostro server MCP con Autenticazione SigV4
• La costruzione dell’agente con un prompt di sistema e gli strumenti del server MCP

Deployment e testing

Prima, compiliamo il codice:

pnpm

pnpm nx run-many --target build --all

yarn

yarn nx run-many --target build --all

npm

npx nx run-many --target build --all

bun

bunx nx run-many --target build --all

Consiglio

Se incontri errori di linting, puoi eseguire questo comando per correggerli automaticamente.

pnpm

pnpm nx run-many --target lint --configuration=fix --all

yarn

yarn nx run-many --target lint --configuration=fix --all

npm

npx nx run-many --target lint --configuration=fix --all

bun

bunx nx run-many --target lint --configuration=fix --all

Ora puoi effettuare il deploy dell’applicazione con:

pnpm

pnpm nx deploy infra dungeon-adventure-infra-sandbox/*

yarn

yarn nx deploy infra dungeon-adventure-infra-sandbox/*

npm

npx nx deploy infra dungeon-adventure-infra-sandbox/*

bun

bunx nx deploy infra dungeon-adventure-infra-sandbox/*

Il deployment richiederà circa 2 minuti.

Al completamento, dovresti vedere output simili a questi (alcuni valori sono stati oscurati):

dungeon-adventure-infra-sandbox-Application
dungeon-adventure-infra-sandbox-Application: deploying... [2/2]

 ✅  dungeon-adventure-infra-sandbox-Application

✨  Deployment time: 354s

Outputs:
dungeon-adventure-infra-sandbox-Application.ElectroDbTableTableNameXXX = dungeon-adventure-infra-sandbox-Application-ElectroDbTableXXX-YYY
dungeon-adventure-infra-sandbox-Application.GameApiEndpointXXX = https://xxx.execute-api.region.amazonaws.com/prod/
dungeon-adventure-infra-sandbox-Application.GameUIDistributionDomainNameXXX = xxx.cloudfront.net
dungeon-adventure-infra-sandbox-Application.InventoryMcpArn = arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventureventoryMcpServerXXXX-YYYY
dungeon-adventure-infra-sandbox-Application.StoryAgentArn = arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventurecationStoryAgentXXXX-YYYY
dungeon-adventure-infra-sandbox-Application.UserIdentityUserIdentityIdentityPoolIdXXX = region:xxx
dungeon-adventure-infra-sandbox-Application.UserIdentityUserIdentityUserPoolIdXXX = region_xxx

Possiamo testare l’API in due modi:

• Avviando un’istanza locale del server Agent e invocandola con curl
• Chiamando l’API deployata usando curl con un token JWT

Local

Avvia il server Agent locale con:

pnpm

PORT=9999 INVENTORY_MCP_ARN=arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventureventoryMcpServerXXXX-YYYY AWS_REGION=<region> pnpm nx run dungeon_adventure.story:agent-serve

yarn

PORT=9999 INVENTORY_MCP_ARN=arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventureventoryMcpServerXXXX-YYYY AWS_REGION=<region> yarn nx run dungeon_adventure.story:agent-serve

npm

PORT=9999 INVENTORY_MCP_ARN=arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventureventoryMcpServerXXXX-YYYY AWS_REGION=<region> npx nx run dungeon_adventure.story:agent-serve

bun

PORT=9999 INVENTORY_MCP_ARN=arn:aws:bedrock-agentcore:region:xxxxxxx:runtime/dungeonadventureventoryMcpServerXXXX-YYYY AWS_REGION=<region> bunx nx run dungeon_adventure.story:agent-serve

Attenzione

Utilizza l’output InventoryMcpArn del deploy CDK sopra, specificando anche la tua AWS_REGION target.

Quando il server è attivo (nessun output visibile!), invocalo con:

curl -N -X POST http://127.0.0.1:9999/invocations \
  -d '{"genre":"superhero", "actions":[], "playerName":"UnnamedHero"}' \
  -H "Content-Type: application/json" \
  -H "X-Amzn-Bedrock-AgentCore-Runtime-Session-Id: abcdefghijklmnopqrstuvwxyz-123456789"

Deployed

Per testare l’agente deployato, autenticati con Cognito per ottenere un token JWT. Imposta prima le variabili d’ambiente:

# Imposta User Pool ID e Client ID dagli output CDK
export POOL_ID="<UserPoolId dagli output CDK>"
export CLIENT_ID="<UserPoolClientId dagli output CDK>"
export REGION="<tua-regione>"

Crea un utente test e ottieni il token:

# Crea utente
aws cognito-idp admin-create-user \
  --user-pool-id $POOL_ID \
  --username "testuser" \
  --temporary-password "TempPass123!" \
  --region $REGION \
  --message-action SUPPRESS > /dev/null

# Imposta password permanente
aws cognito-idp admin-set-user-password \
  --user-pool-id $POOL_ID \
  --username "testuser" \
  --password "PermanentPass123!" \
  --region $REGION \
  --permanent > /dev/null

# Autentica e cattura token
export BEARER_TOKEN=$(aws cognito-idp initiate-auth \
  --client-id "$CLIENT_ID" \
  --auth-flow USER_PASSWORD_AUTH \
  --auth-parameters USERNAME='testuser',PASSWORD='PermanentPass123!' \
  --region $REGION | jq -r '.AuthenticationResult.IdToken')

Attenzione

Utilizza l’output UserIdentityUserIdentityUserPoolIdXXX per POOL_ID e assicurati di avere il Client ID corrispondente dagli output del deployment CDK.

Invoca l’agente deployato usando l’URL Bedrock AgentCore runtime:

# Imposta Story Agent ARN dagli output CDK
export AGENT_ARN="<StoryAgentArn dagli output CDK>"

# Codifica URL dell'ARN
export ENCODED_ARN=$(echo $AGENT_ARN | sed 's/:/%3A/g' | sed 's/\//%2F/g')

# Costruisci URL di invocazione
export MCP_URL="https://bedrock-agentcore.$REGION.amazonaws.com/runtimes/$ENCODED_ARN/invocations?qualifier=DEFAULT"

# Invoca l'agente
curl -N -X POST "$MCP_URL" \
  -H "authorization: Bearer $BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -H "X-Amzn-Bedrock-AgentCore-Runtime-Session-Id: abcdefghijklmnopqrstuvwxyz-123456789" \
  -d '{"genre":"superhero", "actions":[], "playerName":"UnnamedHero"}'

Nota

La codifica URL è necessaria per formattare correttamente l’ARN nell’endpoint API di Bedrock AgentCore.

Se il comando ha successo, vedrai eventi in streaming simili a:

data: {"init_event_loop": true}

data: {"start": true}

data: {"start_event_loop": true}

data: {"event": {"messageStart": {"role": "assistant"}}}

data: {"event": {"contentBlockDelta": {"delta": {"text": "Welcome"}, "contentBlockIndex": 0}}}

...

Complimenti. Hai creato e deployato il tuo primo Strands Agent su Bedrock AgentCore Runtime! 🎉🎉🎉