Vertex Macro｜Financial Cloud Cloud · AWS Amarathon 2025
Vertex Macro｜Amarathon 2025 Recap 03：Multi-Agent on AgentCore
Challenge: Traditional Workflow During a Failure
● Check cloud resource status
● Failure occurs
● View incident history
● Check configuration
● Check alerts
● Assign work order
● Analyze logs
● Hypothesize root cause
● Search for solutions
● Check metrics
● Analyze dependencies
● View recent updates
● Search call chains
● View O&M manual
● Query service dashboard
● Execute mitigation measures
● Notify colleagues
● Query abnormal metrics
● Read manual
● Monitor recovery status
Core Challenges
● Timely loss mitigation
● Fault isolation
● Resolve issues within an acceptable timeframe
● Limit failures within isolation boundaries to prevent cascading effects on other services, thereby reducing the scope of failure impact
● Ensure services meet user expectations and SLAs
Solution Evolution
● Evolution from single agent to multi-agent solutions
● Ideal Solution
System Notifications and Alerts
● Alarm Metric: 5xx rate over 30%
● Alarm Detail: Service, Endpoint, Triggered Time
● Supplementary Root Cause Analysis
Key Findings: API error rate 100%, DB no error, S3 bucket policy was updated.
● Immediate Action: Check S3 bucket policy to Deny
● Confidence: 90%
Confirm Automatic Operations
● Message: System Revered.
● RCA: S3 bucket policy was set to Deny
● MTTR: 5 mins
● Reduce failure recovery time from hours to minutes
SRE Expert Work Scope
● Why did my User Service error rate reach 5% in the past hour?
● Because the RDS MySQL instance experienced 12 connection limit exceeded issues in the past hour
● Familiarize with the current system
● Find service correlations
● Analyze logs
● Analyze audit logs
● Analyze configuration
● Analyze metrics
Consider Two Questions
● 1. If the system complexity is high, the troubleshooting workflow is long, and the log volume is large, can a single agent work smoothly?
● 2. Is it possible to clone SRE expert experience into agents to replace the Q&A method, allowing agents to make autonomous decisions and actions?
Multi-Agent Architecture Design
● "Planner" creates workflows
● "Executor" is responsible for executing assigned tasks
● "Evaluator" is responsible for assessing whether each step's result is beneficial, returning to the "Planner" for subsequent planning
● Results also need to be reviewed by the "Evaluator" before returning
● The "Planner" can adjust the process based on feedback from the "Evaluator"
Multi-Agent vs Single Agent
● 1. Suitable for more complex tasks
● 2. Clearer responsibility and permission boundaries
● 3. Easier context engineering
● 4. More convenient scaling
● AgentCore Best Practices
Introduction to Agent-Specific Runtime Environment
● Challenges from "trial" to "implementation"
● Challenges from PoC to production environment implementation
● Performance
● Elasticity
● Security
● Creating business value
● Compliance
Agent Runtime Environment v1.0
● INTERFACES & PROTOCOLS (MCP/A2A)
● Agent Deployment
● Agent Framework
● Large Language Model
● Memory
● Prompts
● Tools/Resources
● Guardrails
● Observability
● Evaluation
Agent Runtime Environment v2.0
● INTERFACES & PROTOCOLS (MCP/A2A)
● Agent Deployment
● Amazon Bedrock
● AgentCore
● Agent Framework
● Large Language Model Runtime
● Memory
● Prompts
● Tools/Resources
● Identity Tools
● Guardrails
● Gateway
● Observability
● Evaluation
SPECIALIZED
● Amazon Q Agents
FULLY-MANAGED
● Amazon Bedrock Agents
DIY
● OSS Frameworks, Strands Agents SDK