AWS Bedrock AgentCore vs Google ADK: Complete Guide to Production AI Agents

Everyone's building AI agents. Few are running them in production. The gap between a LangChain prototype and a scalable enterprise system is where most teams fail. AWS Bedrock AgentCore and Google ADK are changing this—but which one should you choose, and how do you actually deploy it?

What is AWS Bedrock AgentCore?

AWS Bedrock AgentCore is a managed runtime environment for deploying AI agents at enterprise scale. It provides framework-agnostic hosting (supports LangGraph, CrewAI, Strands, or custom frameworks), integrated memory management, built-in authorization via Amazon Cognito, and native observability through CloudWatch. AgentCore handles the infrastructure complexity so teams can focus on agent logic rather than scaling challenges.

What is Google ADK (Agent Development Kit)?

Google ADK is Google Cloud's framework for building and deploying AI agents on Vertex AI. It provides tools for agent orchestration, integration with Gemini models, and deployment to Google Cloud's infrastructure. ADK emphasizes rapid prototyping with production-ready patterns baked in.

1. Why Production AI Agents Are Hard

Building a demo agent takes hours. Deploying one to production takes months—if you don't have the right infrastructure.

Here's what breaks when you move from prototype to production:

Memory Management Demo agents are stateless. Production agents need to remember context across sessions, maintain conversation history, and handle multiple concurrent users without cross-contamination.

Authorization & Security Who can access which agent? How do you prevent prompt injection? How do you audit every interaction for compliance? These aren't concerns in a Jupyter notebook.

Scalability Your demo handles 10 requests per minute. Production needs to handle 10,000. Auto-scaling, load balancing, and graceful degradation become critical.

Observability When an agent gives a wrong answer, you need to trace exactly why. Which tool was called? What context was retrieved? Where did the reasoning break down?

Framework Lock-in You built with LangChain. Now you want to try CrewAI. Rewriting infrastructure is expensive.

AWS AgentCore and Google ADK solve these problems—but in different ways.

2. AWS Bedrock AgentCore: Architecture Deep Dive

AgentCore is AWS's answer to the "prototype to production" gap. It's not a framework—it's a runtime that hosts any framework.

Core Components

Runtime Layer

• Hosts agent code in Lambda or custom containers
• Framework-agnostic: supports LangGraph, CrewAI, Strands, custom Python
• Serverless scaling by default, Kubernetes-style deployments available

Memory Layer

• Scalable, persistent memory across sessions
• Session isolation (User A never sees User B's context)
• Automatic context windowing for long conversations

Identity Layer (Cognito Integration)

• Enterprise SSO support
• Fine-grained permissions per agent
• Audit trails for every interaction

Gateway Layer

• REST and WebSocket APIs
• Rate limiting and throttling
• Request/response logging

Observability Layer

• CloudWatch integration for metrics and logs
• Distributed tracing across agent-to-agent calls
• Cost attribution per agent

Tool Layer

• Browser automation tool (built-in)
• Code interpreter (Python execution sandbox)
• Custom tool registration

Architecture Diagram

Key Advantages

1. Framework Agnostic: Swap LangGraph for CrewAI without infrastructure changes
2. Model Agnostic: Use Bedrock models or connect external LLMs (OpenAI, Anthropic API)
3. Enterprise-Ready: Cognito SSO, VPC isolation, compliance certifications
4. Serverless by Default: Pay per invocation, auto-scales to zero

3. Google ADK: Architecture Deep Dive

Google ADK takes a different approach—tighter integration with Google Cloud's AI stack, especially Vertex AI and Gemini.

Core Components

Agent Builder

• Visual interface for defining agent flows
• Code-first option with Python SDK
• Built-in templates for common patterns

Orchestration Engine

• Native support for multi-agent coordination
• Automatic context passing between agents
• Built-in retry and fallback logic

Vertex AI Integration

• Seamless access to Gemini models
• Grounding with Google Search
• Integration with Vertex AI Search (RAG)

Deployment Options

• Cloud Run for serverless
• GKE for container orchestration
• Cloud Functions for simple agents

Architecture Diagram

Key Advantages

1. Gemini-First: Best-in-class integration with Google's latest models
2. Grounding Built-In: Connect to Google Search or enterprise data instantly
3. Visual Builder: Non-engineers can modify agent flows
4. Google Cloud Native: If you're already on GCP, minimal new infrastructure

4. Head-to-Head Comparison

5. When to Choose AgentCore vs ADK

Choose AWS Bedrock AgentCore When:

✅ You need framework flexibility Your team experiments with LangGraph, CrewAI, and custom frameworks. AgentCore lets you swap without rewriting infrastructure.

✅ You're already on AWS Existing VPCs, IAM roles, and CloudWatch dashboards integrate seamlessly.

✅ Enterprise compliance is critical AgentCore supports FedRAMP, HIPAA, SOC2, and AWS's compliance certifications.

✅ You want model optionality Today you use Claude, tomorrow you might need Llama for cost optimization. AgentCore supports both.

✅ You need sophisticated memory management Built-in session isolation, persistent memory, and context windowing out of the box.

Choose Google ADK When:

✅ Gemini is your primary model ADK's Gemini integration is unmatched. If you're betting on Google's models, ADK is the natural choice.

✅ You need Google Search grounding Real-time grounding with Google Search is built-in—powerful for agents that need current information.

✅ You want visual agent building Non-engineers can modify agent flows through ADK's visual builder.

✅ You're already on Google Cloud Existing Vertex AI, BigQuery, and Cloud Storage integrations work seamlessly.

✅ Rapid prototyping is priority ADK's templates and visual builder accelerate time-to-first-agent.

The Real Decision Framework

Ask yourself:

1. Primary model? Gemini → ADK. Claude/mixed → AgentCore
2. Framework preference? LangGraph/CrewAI → AgentCore. Native/visual → ADK
3. Cloud platform? AWS → AgentCore. GCP → ADK
4. Team composition? Mostly engineers → AgentCore. Mixed technical/non-technical → ADK

6. Production Implementation: AgentCore

Let's build a real agent on AgentCore. We'll create a Financial Advisor with guardrails—the same pattern we deployed at Cognilium.

Step 1: Define Your Agent Configuration

# agent_config.py
from bedrock_agentcore import Agent, Tool, Guardrail

# Define the Financial Advisor agent
financial_advisor = Agent(
    name="FinancialAdvisor",
    description="Provides personalized financial guidance and budget planning",
    model="anthropic.claude-3-sonnet",
    system_prompt="""You are a financial advisor assistant. 
    Provide helpful, accurate financial guidance.
    Always recommend consulting a licensed professional for major decisions.
    Never provide specific investment recommendations or guarantee returns.""",
    tools=[
        Tool(
            name="calculate_budget",
            description="Calculate budget allocations based on income and expenses",
            handler="tools.budget_calculator"
        ),
        Tool(
            name="retirement_projection",
            description="Project retirement savings based on current trajectory",
            handler="tools.retirement_projector"
        )
    ],
    guardrails=[
        Guardrail(
            name="compliance_filter",
            type="input_output",
            handler="guardrails.financial_compliance"
        )
    ]
)

Step 2: Implement Guardrails

# guardrails/financial_compliance.py
BLOCKED_TOPICS = [
    "money laundering",
    "offshore accounts without reporting",
    "tax evasion",
    "insider trading",
    "market manipulation"
]

def financial_compliance(input_text: str, output_text: str = None) -> dict:
    """
    Check for financial compliance violations.
    Returns: {"allowed": bool, "reason": str}
    """
    text_to_check = input_text.lower()
    if output_text:
        text_to_check += " " + output_text.lower()
    
    for topic in BLOCKED_TOPICS:
        if topic in text_to_check:
            return {
                "allowed": False,
                "reason": f"Request involves prohibited topic: {topic}. "
                          f"Please consult a licensed professional for legitimate needs."
            }
    
    return {"allowed": True, "reason": ""}

Step 3: Configure Memory

# memory_config.py
from bedrock_agentcore import MemoryConfig

memory = MemoryConfig(
    type="session",
    ttl_hours=24,
    max_tokens=8000,
    isolation="user",  # Each user has isolated memory
    persistence="dynamodb",
    context_window_strategy="sliding"  # Keep most recent context
)

Step 4: Deploy to AgentCore

# Deploy using AgentCore CLI
agentcore deploy \
  --agent financial_advisor \
  --memory-config memory_config.py \
  --runtime lambda \
  --region us-east-1 \
  --auth cognito \
  --cognito-pool-id us-east-1_xxxxx

Step 5: Test the Deployment

# test_agent.py
import requests

API_ENDPOINT = "https://your-agentcore-endpoint.amazonaws.com/agent"
AUTH_TOKEN = "your-cognito-token"

# Test legitimate request
response = requests.post(
    API_ENDPOINT,
    headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
    json={
        "message": "Help me plan my retirement budget for the next 5 years",
        "session_id": "user-123-session-1"
    }
)
print("Legitimate request:", response.json())

# Test guardrail trigger
response = requests.post(
    API_ENDPOINT,
    headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
    json={
        "message": "How can I move money offshore without reporting it?",
        "session_id": "user-123-session-1"
    }
)
print("Blocked request:", response.json())
# Expected: {"blocked": true, "reason": "Request involves prohibited topic..."}

7. Production Implementation: Google ADK

Now let's build the same Financial Advisor on Google ADK.

Step 1: Define Agent with ADK SDK

# agent.py
from google.cloud import aiplatform
from vertexai.preview import reasoning_engines

# Initialize Vertex AI
aiplatform.init(project="your-project-id", location="us-central1")

# Define the agent
agent = reasoning_engines.LangchainAgent(
    model="gemini-1.5-pro",
    system_instruction="""You are a financial advisor assistant.
    Provide helpful, accurate financial guidance.
    Always recommend consulting a licensed professional for major decisions.
    Never provide specific investment recommendations or guarantee returns.""",
    tools=[budget_calculator, retirement_projector],
)

# Create the reasoning engine
remote_agent = reasoning_engines.ReasoningEngine.create(
    agent,
    requirements=[
        "google-cloud-aiplatform[reasoningengine,langchain]",
        "cloudpickle==3.0.0",
        "langchain==0.2.0",
    ],
    display_name="Financial Advisor Agent",
)

Step 2: Implement Tools

# tools.py
def budget_calculator(
    monthly_income: float,
    fixed_expenses: float,
    savings_goal_percent: float
) -> dict:
    """
    Calculate budget allocation based on income and expenses.
    
    Args:
        monthly_income: Monthly income in dollars
        fixed_expenses: Total fixed monthly expenses
        savings_goal_percent: Target savings percentage (0-100)
    
    Returns:
        Budget breakdown with recommendations
    """
    disposable = monthly_income - fixed_expenses
    savings_target = monthly_income * (savings_goal_percent / 100)
    discretionary = disposable - savings_target
    
    return {
        "monthly_income": monthly_income,
        "fixed_expenses": fixed_expenses,
        "savings_target": savings_target,
        "discretionary_spending": max(0, discretionary),
        "recommendation": "healthy" if discretionary > 0 else "reduce expenses"
    }

Step 3: Deploy to Cloud Run

# Deploy using gcloud
gcloud run deploy financial-advisor \
  --image gcr.io/your-project/financial-advisor:latest \
  --region us-central1 \
  --allow-unauthenticated=false \
  --service-account=agent-sa@your-project.iam.gserviceaccount.com

Step 4: Query the Agent

# query.py
response = remote_agent.query(
    input="Help me plan my retirement budget for the next 5 years. "
          "I make $8,000/month and spend $5,000 on fixed expenses."
)
print(response)

8. Multi-Agent Orchestration Patterns

Real production systems rarely use a single agent. Here's how to orchestrate multiple agents.

Pattern 1: Router Agent (Hub and Spoke)

The router analyzes the query and delegates to specialized agents.

Pattern 2: Sequential Pipeline

Each agent handles one stage, passing context to the next.

Pattern 3: Parallel Specialists

At Cognilium, VectorHire uses this pattern with 4 parallel agents processing candidate data simultaneously.

Implementation: AgentCore Multi-Agent

# multi_agent_config.py
from bedrock_agentcore import AgentOrchestrator, Agent

orchestrator = AgentOrchestrator(
    name="FinancialAssistant",
    routing_strategy="classifier",  # Use LLM to route
    agents=[
        Agent(name="FinancialAdvisor", ...),
        Agent(name="BudgetPlanner", ...),
        Agent(name="ComplianceGuard", ...),
    ],
    fallback_agent="FinancialAdvisor",
    max_agent_hops=3,  # Prevent infinite loops
    trace_enabled=True
)

9. Memory Management Strategies

Memory is where most agent deployments break. Here's how to get it right.

Strategy 1: Session-Scoped Memory

# Best for: Customer support, short interactions
memory_config = {
    "scope": "session",
    "ttl": "2 hours",
    "max_messages": 50,
    "summary_after": 20  # Summarize older messages
}

Strategy 2: User-Scoped Persistent Memory

# Best for: Personal assistants, ongoing relationships
memory_config = {
    "scope": "user",
    "ttl": "30 days",
    "storage": "dynamodb",  # or Cloud Firestore
    "context_window": 8000,  # tokens
    "retrieval": "semantic"  # Retrieve relevant past context
}

Strategy 3: Hybrid (Session + Long-term)

# Best for: Financial advisors, complex workflows
memory_config = {
    "session_memory": {
        "ttl": "4 hours",
        "max_messages": 100
    },
    "long_term_memory": {
        "storage": "vector_db",  # Pinecone, Weaviate
        "retrieval_k": 5,  # Top 5 relevant memories
        "importance_threshold": 0.7
    }
}

10. Observability and Debugging

You can't fix what you can't see. Here's how to trace agent behavior.

AgentCore: CloudWatch Integration

# Every agent invocation logs to CloudWatch
# View in CloudWatch Logs Insights:

fields @timestamp, @message
| filter agent_name = "FinancialAdvisor"
| filter @message like /error/
| sort @timestamp desc
| limit 100

Tracing Agent-to-Agent Handoffs

[2025-01-15T10:23:45Z] REQUEST_RECEIVED
  session_id: user-123-session-456
  message: "Help me plan my retirement..."

[2025-01-15T10:23:45Z] ROUTER_DECISION
  routed_to: BudgetPlanner
  confidence: 0.92
  reasoning: "Query involves budget planning task"

[2025-01-15T10:23:46Z] AGENT_INVOCATION
  agent: BudgetPlanner
  input_tokens: 450
  
[2025-01-15T10:23:48Z] TOOL_CALL
  tool: retirement_projection
  params: {income: 8000, years: 5}
  
[2025-01-15T10:23:48Z] TOOL_RESPONSE
  result: {projected_savings: 180000, monthly_contribution: 3000}

[2025-01-15T10:23:50Z] RESPONSE_GENERATED
  output_tokens: 380
  latency_ms: 5200

11. Cost Comparison

Real production costs from our deployments:

AWS Bedrock AgentCore

Google ADK

Note: Gemini is currently cheaper per token, but Claude often requires fewer tokens to complete tasks. Your actual costs depend on your specific use case.

Cost Optimization Tips

1. Use smaller models for routing: Route with Claude Haiku or Gemini Flash, execute with larger models
2. Implement caching: Cache frequent queries (saves 30-50% on token costs)
3. Optimize prompts: Shorter system prompts = lower costs per request
4. Set token limits: Cap output length to prevent runaway costs

12. Real Production Example: Financial Advisor System

At Cognilium, we deployed a multi-agent Financial Advisor on AWS Bedrock AgentCore. Here's the architecture and results.

The System

3 Specialized Agents:

1. Financial Advisor Agent: Handles general financial questions
2. Budget Planning Agent: Detailed budget calculations and projections
3. Guardrails Agent: Filters illegal/non-compliant requests

Architecture:

• Runtime: Lambda (serverless)
• Memory: DynamoDB with 24-hour session TTL
• Auth: Cognito with enterprise SSO
• Observability: CloudWatch with custom dashboards

Results

Key Learnings

1. Guardrails are essential: 2.3% of requests triggered our compliance filter—real attempts to extract illegal advice
2. Memory isolation matters: One user's financial data should never leak to another
3. Observability saves debugging time: Full traces reduced debugging time from hours to minutes

13. Common Mistakes (And How to Avoid Them)

Mistake 1: No Memory Isolation

Problem: User A's financial data appears in User B's responses.

Solution: Always configure user-level memory isolation:

memory_config = {"isolation": "user", "encryption": "at_rest_and_transit"}

Mistake 2: Missing Guardrails

Problem: Agent provides advice on illegal activities.

Solution: Implement input AND output guardrails:

guardrails = [
    Guardrail(type="input", handler=compliance_check),
    Guardrail(type="output", handler=compliance_check)
]

Mistake 3: No Fallback Strategy

Problem: Agent crashes and users see error messages.

Solution: Configure graceful degradation:

fallback_response = "I'm having trouble processing that. Let me connect you with a human advisor."

Mistake 4: Ignoring Token Costs

Problem: Monthly bill 10x higher than expected.

Solution:

• Set max_tokens limits
• Use cheaper models for routing
• Implement response caching

Mistake 5: No Tracing

Problem: "The agent gave wrong advice" but no way to debug.

Solution: Enable full tracing from day one:

config = {"trace_enabled": True, "log_level": "DEBUG"}

14. Getting Started: Your First Production Agent

Quick Start: AgentCore (30 minutes)

1. Prerequisites:

   • AWS account with Bedrock access
   • Cognito user pool (for auth)
   • Python 3.9+

2. Install CLI:

   pip install bedrock-agentcore-cli
   

3. Initialize project:

   agentcore init my-first-agent
   

4. Deploy:

   agentcore deploy --runtime lambda
   

Quick Start: Google ADK (30 minutes)

1. Prerequisites:

   • GCP project with Vertex AI enabled
   • gcloud CLI installed
   • Python 3.9+

2. Install SDK:

   pip install google-cloud-aiplatform[reasoningengine]
   

3. Create agent:

   from vertexai.preview import reasoning_engines
   agent = reasoning_engines.LangchainAgent(model="gemini-1.5-pro", ...)
   

4. Deploy:

   remote_agent = reasoning_engines.ReasoningEngine.create(agent, ...)
   

Next Steps

Ready to build? Here's your path forward:

1. Getting Started with AWS Bedrock AgentCore → Step-by-step setup guide with code examples

2. Google ADK Tutorial → Build your first ADK agent in 30 minutes

3. AgentCore Memory Layer Deep Dive → Master stateful agents

4. Multi-Agent Orchestration Patterns → LangGraph vs CrewAI vs Native comparison

5. AgentCore Observability Guide → Production monitoring and debugging

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