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).

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.

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.

Deployment Decision Matrix

Multi-Agent Pattern Selection

Recommendation: Start with single agents, evolve as needed.

See architecture.md for examples.

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

#-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

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

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.