aws-strands-agents-agentcore

AWS Strands Agents & AgentCore

Overview

AWS Strands Agents SDK: Open-source Python framework for building AI agents with model-driven orchestration (minimal code, model decides tool usage)

Amazon Bedrock AgentCore: Enterprise platform for deploying, operating, and scaling agents in production

Relationship: Strands SDK runs standalone OR with AgentCore platform services. AgentCore is optional but provides enterprise features (8hr runtime, streaming, memory, identity, observability).

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Quick Start Decision Tree

What are you building?

Single-purpose agent:

• Event-driven (S3, SQS, scheduled) → Lambda deployment
• Interactive with streaming → AgentCore Runtime
• API endpoint (stateless) → Lambda

Multi-agent system:

• Deterministic workflow → Graph Pattern
• Autonomous collaboration → Swarm Pattern
• Simple delegation → Agent-as-Tool Pattern

Tool/Integration Server (MCP):

• ALWAYS deploy to ECS/Fargate or AgentCore Runtime
• NEVER Lambda (stateful, needs persistent connections)

See architecture.md for deployment examples.

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Critical Constraints

MCP Server Requirements

1. Transport: MUST use streamable-http (NOT stdio)
2. Endpoint: MUST be at 0.0.0.0:8000/mcp
3. Deployment: MUST be ECS/Fargate or AgentCore Runtime (NEVER Lambda)
4. Headers: Must accept application/json and text/event-stream

Why: MCP servers are stateful and need persistent connections. Lambda is ephemeral and unsuitable.

See limitations.md for details.

Tool Count Limits

• Models struggle with > 50-100 tools
• Solution: Implement semantic search for dynamic tool loading

See patterns.md for implementation.

Token Management

• Claude 4.5: 200K context (use ~180K max)
• Long conversations REQUIRE conversation managers
• Multi-agent costs multiply 5-10x

See limitations.md for strategies.

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Deployment Decision Matrix

Component	Lambda	ECS/Fargate	AgentCore Runtime
Stateless Agents	✅ Perfect	❌ Overkill	❌ Overkill
Interactive Agents	❌ No streaming	⚠️ Possible	✅ Ideal
MCP Servers	❌ NEVER	✅ Standard	✅ With features
Duration	< 15 minutes	Unlimited	Up to 8 hours
Cold Starts	Yes (30-60s)	No	No

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Multi-Agent Pattern Selection

Pattern	Complexity	Predictability	Cost	Use Case
Single Agent	Low	High	1x	Most tasks
Agent as Tool	Low	High	2-3x	Simple delegation
Graph	High	Very High	3-5x	Deterministic workflows
Swarm	Medium	Low	5-8x	Autonomous collaboration

Recommendation: Start with single agents, evolve as needed.

See architecture.md for examples.

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When to Read Reference Files

patterns.md

• Base agent factory patterns (reusable components)
• MCP server registry patterns (tool catalogues)
• Semantic tool search (> 50 tools)
• Tool design best practices
• Security patterns
• Testing patterns

observability.md

• AWS AgentCore Observability Platform setup
• Runtime-hosted vs self-hosted configuration
• Session tracking for multi-turn conversations
• OpenTelemetry setup
• Cost tracking hooks
• Production observability patterns

evaluations.md

• AWS AgentCore Evaluations - Quality assessment with LLM-as-a-Judge
• 13 built-in evaluators (Helpfulness, Correctness, GoalSuccessRate, etc.)
• Custom evaluators with your own prompts and models
• Online (continuous) and on-demand evaluation modes
• CloudWatch integration and alerting

limitations.md

• MCP server deployment issues
• Tool selection problems (> 50 tools)
• Token overflow
• Lambda limitations
• Multi-agent cost concerns
• Throttling errors
• Cold start latency

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#-Driven Philosophy

Key Concept: Strands Agents delegates orchestration to the model rather than requiring explicit control flow code.

# Traditional: Manual orchestration (avoid)
while not done:
    if needs_research:
        result = research_tool()
    elif needs_analysis:
        result = analysis_tool()

# Strands: Model decides (prefer)
agent = Agent(
    system_prompt="You are a research analyst. Use tools to answer questions.",
    tools=[research_tool, analysis_tool]
)
result = agent("What are the top tech trends?")
 automatically orchestrates: research_tool → analysis_tool → respond

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Selection

Primary Provider: Anthropic Claude via AWS Bedrock

Model ID Format: anthropic.claude-{model}-{version}

Current Models (as of January 2025):

• anthropic.claude-sonnet-4-5-20250929-v1:0 - Production
• anthropic.claude-haiku-4-5-20251001-v1:0 - Fast/economical
• anthropic.claude-opus-4-5-20250514-v1:0 - Complex reasoning

Check Latest Models:

aws bedrock list-foundation-models --by-provider anthropic \
  --query 'modelSummaries[*].[modelId,modelName]' --output table

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Quick Examples

Basic Agent

from strands import Agent
from strands.models import BedrockModel
from strands.session import DynamoDBSessionManager
from strands.agent.conversation_manager import SlidingWindowConversationManager

agent = Agent(
    agent_id="my-agent",
    model=BedrockModel(model_id="anthropic.claude-sonnet-4-5-20250929-v1:0"),
    system_prompt="You are helpful.",
    tools=[tool1, tool2],
    session_manager=DynamoDBSessionManager(table_name="sessions"),
    conversation_manager=SlidingWindowConversationManager(max_messages=20)
)

result = agent("Process this request")

See patterns.md for base agent factory patterns.

MCP Server (ECS/Fargate)

from mcp.server import FastMCP
import psycopg2.pool

# Persistent connection pool (why Lambda won't work)
db_pool = psycopg2.pool.SimpleConnectionPool(minconn=1, maxconn=10, host="db.internal")

mcp = FastMCP("Database Tools")

@mcp.tool()
def query_database(sql: str) -> dict:
    conn = db_pool.getconn()
    try:
        cursor = conn.cursor()
        cursor.execute(sql)
        return {"status": "success", "rows": cursor.fetchall()}
    finally:
        db_pool.putconn(conn)

# CRITICAL: streamable-http mode
if __name__ == "__main__":
    mcp.run(transport="streamable-http", host="0.0.0.0", port=8000)

See architecture.md for deployment details.

Tool Error Handling

from strands import tool

@tool
def safe_tool(param: str) -> dict:
    """Always return structured results, never raise exceptions."""
    try:
        result = operation(param)
        return {"status": "success", "content": [{"text": str(result)}]}
    except Exception as e:
        return {"status": "error", "content": [{"text": f"Failed: {str(e)}"}]}

See patterns.md for tool design patterns.

Observability

AgentCore Runtime (Automatic):

# Install with OTEL support
# pip install 'strands-agents[otel]'
# Add 'aws-opentelemetry-distro' to requirements.txt

from bedrock_agentcore.runtime import BedrockAgentCoreApp

app = BedrockAgentCoreApp()
agent = Agent(...)  # Automatically instrumented

@app.entrypoint
def handler(payload):
    return agent(payload["prompt"])

Self-Hosted:

export AGENT_OBSERVABILITY_ENABLED=true
export OTEL_PYTHON_DISTRO=aws_distro
export OTEL_RESOURCE_ATTRIBUTES="service.name=my-agent"

opentelemetry-instrument python agent.py

General OpenTelemetry:

from strands.observability import StrandsTelemetry

# Development
telemetry = StrandsTelemetry().setup_console_exporter()

# Production
telemetry = StrandsTelemetry().setup_otlp_exporter()

See observability.md for detailed patterns.

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Session Storage Selection

Local dev         → FileSystem
Lambda agents     → S3 or DynamoDB
ECS agents        → DynamoDB
Interactive chat  → AgentCore Memory
Knowledge bases   → AgentCore Memory

See architecture.md for storage backend comparison.

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When to Use AgentCore Platform vs SDK Only

Use Strands SDK Only

• Simple, stateless agents
• Tight cost control required
• No enterprise features needed
• Want deployment flexibility

Use Strands SDK + AgentCore Platform

• Need 8-hour runtime support
• Streaming responses required
• Enterprise security/compliance
• Cross-session intelligence needed
• Want managed infrastructure

See architecture.md for platform service details.

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Common Anti-Patterns

1. ❌ Overloading agents with > 50 tools → Use semantic search
2. ❌ No conversation management → Implement SlidingWindow or Summarising
3. ❌ Deploying MCP servers to Lambda → Use ECS/Fargate
4. ❌ No timeout configuration → Set execution limits everywhere
5. ❌ Ignoring token limits → Implement conversation managers
6. ❌ No cost monitoring → Implement cost tracking from day one

See patterns.md and limitations.md for details.

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Production Checklist

Before deploying:

• Conversation management configured
• AgentCore Observability enabled or OpenTelemetry configured
• AgentCore Evaluations configured for quality monitoring
• Observability hooks implemented
• Cost tracking enabled
• Error handling in all tools
• Security permissions validated
• MCP servers deployed to ECS/Fargate
• Timeout limits set
• Session backend configured (DynamoDB for production)
• CloudWatch alarms configured

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Reference Files Navigation

• architecture.md - Deployment patterns, multi-agent orchestration, session storage, AgentCore services
• patterns.md - Foundation components, tool design, security, testing, performance optimisation
• limitations.md - Known constraints, workarounds, mitigation strategies, challenges
• observability.md - AgentCore Observability platform, ADOT, GenAI dashboard, OpenTelemetry, hooks, cost tracking
• evaluations.md - AgentCore Evaluations, built-in evaluators, custom evaluators, quality monitoring

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Key Takeaways

1. MCP servers MUST use streamable-http, NEVER Lambda
2. Use semantic search for > 15 tools
3. Always implement conversation management
4. Multi-agent costs multiply 5-10x (track from day one)
5. Set timeout limits everywhere
6. Error handling in tools is non-negotiable
7. Lambda for stateless, AgentCore for interactive
8. AgentCore Observability and Evaluations for production
9. Start simple, evolve complexity
10. Security by default
11. Separate config from code