iSharkFly-Docs/amazon-bedrock-agentcore-samples

mirror of https://github.com/awslabs/amazon-bedrock-agentcore-samples.git synced 2025-09-08 20:50:46 +00:00

feat(02-use-cases): integrate AgentCore Memory with SRE Agent for intelligent context-aware incident response (#210 )

* feat: integrate long-term memory system into SRE agent

- Add AgentCore Memory integration with three memory strategies:
  * User preferences (escalation, notification, workflow preferences)
  * Infrastructure knowledge (dependencies, patterns, baselines)
  * Investigation summaries (timeline, actions, findings)

- Implement memory tools for save/retrieve operations
- Add automatic memory capture through hooks and pattern recognition
- Extend agent state to support memory context
- Integrate memory-aware planning in supervisor agent
- Add comprehensive test coverage for memory functionality
- Create detailed documentation with usage examples

This transforms the SRE agent from stateless to learning assistant
that becomes more valuable over time by remembering user preferences,
infrastructure patterns, and investigation outcomes.

Addresses issue #164

* feat: environment variable config, agent routing fixes, and project organization

- Move USER_ID/SESSION_ID from metadata parsing to environment variables
- Add .memory_id to .gitignore for local memory state
- Update .gitignore to use .scratchpad/ folder instead of .scratchpad.md
- Fix agent routing issues with supervisor prompt and graph node naming
- Add conversation memory tracking for all agents and supervisor
- Improve agent metadata system with centralized constants
- Add comprehensive logging and debugging for agent tool access
- Update deployment script to pass user_id/session_id in payload
- Create .scratchpad/ folder structure for better project organization

* feat: enhance SRE agent with automatic report archiving and error fixes

- Add automatic archiving system for reports by date
- Include user_id in report filenames for better organization
- Fix Pydantic validation error with string-to-list conversion for investigation steps
- Add content length truncation for memory storage to prevent validation errors
- Remove status line from report output for cleaner formatting
- Implement date-based folder organization (YYYY-MM-DD format)
- Add memory content length limits configuration in constants

Key improvements:
- Reports now auto-archive old files when saving new ones
- User-specific filenames: query_user_id_UserName_YYYYMMDD_HHMMSS.md
- Robust error handling for memory content length limits
- Backward compatibility with existing filename formats

* feat: fix memory retrieval system for cross-session searches and user personalization

Key fixes and improvements:
- Fix case preservation in actor_id sanitization (Carol remains Carol, not carol)
- Enable cross-session memory searches for infrastructure and investigation memories
- Add XML parsing support for investigation summaries stored in XML format
- Enhance user preference integration throughout the system
- Add comprehensive debug logging for memory retrieval processes
- Update prompts to support user-specific communication styles and preferences

Memory system now properly:
- Preserves user case in memory namespaces (/sre/users/Carol vs /sre/users/carol)
- Searches across all sessions for planning context vs session-specific for current state
- Parses both JSON and XML formatted investigation memories
- Adapts investigation approach based on user preferences and historical patterns
- Provides context-aware planning using infrastructure knowledge and past investigations

* feat: enhance SRE agent with user-specific memory isolation and anti-hallucination measures

Memory System Improvements:
- Fix memory isolation to retrieve only user-specific memories (Alice doesn't see Carol's data)
- Implement proper namespace handling for cross-session vs session-specific searches
- Add detailed logging for memory retrieval debugging and verification
- Remove verbose success logs, keep only error logs for cleaner output

Anti-Hallucination Enhancements:
- Add tool output validation requirements to agent prompts
- Implement timestamp fabrication prevention (use 2024-* format from backend)
- Require tool attribution for all metrics and findings in reports
- Add backend data alignment patterns for consistent data references
- Update supervisor aggregation prompts to flag unverified claims

Code Organization:
- Extract hardcoded prompts from supervisor.py to external prompt files
- Add missing session_id parameters to SaveInfrastructureTool and SaveInvestigationTool
- Improve memory client namespace documentation and cross-session search logic
- Reduce debug logging noise while maintaining error tracking

Verification Complete:
- Memory isolation working correctly (only user-specific data retrieval)
- Cross-session memory usage properly configured for planning and investigations
- Memory integration confirmed in report generation pipeline
- Anti-hallucination measures prevent fabricated metrics and timestamps

* feat: organize utility scripts in dedicated scripts folder

Script Organization:
- Move manage_memories.py to scripts/ folder with updated import paths
- Move configure_gateway.sh to scripts/ folder with corrected PROJECT_ROOT path
- Copy user_config.yaml to scripts/ folder for self-contained script usage

Path Fixes:
- Update manage_memories.py to import sre_agent module from correct relative path
- Fix .memory_id file path resolution for new script location
- Update configure_gateway.sh PROJECT_ROOT to point to correct parent directory
- Add fallback logic to find user_config.yaml in scripts/ or project root

Script Improvements:
- Update help text and examples to use 'uv run python scripts/' syntax
- Make manage_memories.py executable with proper permissions
- Maintain backward compatibility for custom config file paths
- Self-contained scripts folder with all required dependencies

Verification:
- All scripts work correctly from new location
- Memory management functions operate properly
- Gateway configuration handles paths correctly
- User preferences loading works from scripts directory

* docs: update SSL certificate paths to use /opt/ssl standard location

- Update README.md to reference /opt/ssl for SSL certificate paths
- Update docs/demo-environment.md to use /opt/ssl paths
- Clean up scripts/configure_gateway.sh SSL fallback paths
- Remove duplicate and outdated SSL path references
- Establish /opt/ssl as the standard SSL certificate location

This ensures consistent SSL certificate management across all
documentation and scripts, supporting the established /opt/ssl
directory with proper ubuntu:ubuntu ownership.

* feat: enhance memory system with infrastructure parsing fix and user personalization analysis

Infrastructure Memory Parsing Improvements:
- Fix infrastructure memory parsing to handle both JSON and plain text formats
- Convert plain text memories to structured InfrastructureKnowledge objects
- Change warning logs to debug level for normal text-to-structure conversion
- Ensure all infrastructure memories are now retrievable and usable

User Personalization Documentation:
- Add comprehensive memory system analysis comparing Alice vs Carol reports
- Create docs/examples/ folder with real investigation reports demonstrating personalization
- Document side-by-side communication differences based on user preferences
- Show how same technical incident produces different reports for different user roles

Example Reports Added:
- Alice's technical detailed investigation report (technical role preferences)
- Carol's business-focused executive summary report (executive role preferences)
- Memory system analysis with extensive side-by-side comparisons

This demonstrates the memory system's ability to:
- Maintain technical accuracy while adapting presentation style
- Apply user-specific escalation procedures and communication channels
- Build institutional knowledge about recurring infrastructure patterns
- Personalize identical technical incidents for different organizational roles

* feat: enhance memory system with automatic pattern extraction and improved logging

## Memory System Enhancements
- **Individual agent memory integration**: Every agent response now triggers automatic memory pattern extraction through on_agent_response() hooks
- **Enhanced conversation logging**: Added detailed message breakdown showing USER/ASSISTANT/TOOL message counts and tool names called
- **Fixed infrastructure extraction**: Resolved hardcoded agent name issues by using SREConstants for agent identification
- **Comprehensive memory persistence**: All agent responses and tool executions stored as conversation memory with proper session tracking

## Tool Architecture Clarification
- **Centralized memory access**: Confirmed only supervisor agent has direct access to memory tools (retrieve_memory, save_*)
- **Individual agent focus**: Individual agents have NO memory tools, only domain-specific tools (5 tools each for metrics, logs, k8s, runbooks)
- **Automatic pattern recognition**: Memory capture happens automatically through hooks, not manual tool calls by individual agents

## Documentation Updates
- **Updated memory-system.md**: Comprehensive design documentation reflecting current implementation
- **Added example analyses**: Created flight-booking-analysis.md and api-response-time-analysis.md in docs/examples/
- **Enhanced README.md**: Added memory system overview and personalized investigation examples
- **Updated .gitignore**: Now ignores entire reports/ folder instead of just .md files

## Implementation Improvements
- **Event ID tracking**: All memory operations generate and log event IDs for verification
- **Pattern extraction confirmation**: Logs confirm pattern extraction working for all agent types
- **Memory save verification**: Comprehensive logging shows successful saves across all memory types
- **Script enhancements**: manage_memories.py now handles duplicate removal and improved user management

* docs: enhance memory system documentation with planning agent memory usage examples

- Add real agent.log snippets showing planning agent retrieving and using memory context
- Document XML-structured prompts for improved Claude model interaction
- Explain JSON response format enforcement and infrastructure knowledge extraction
- Add comprehensive logging and monitoring details
- Document actor ID design for proper memory namespace isolation
- Fix ASCII flow diagram alignment for better readability
- Remove temporal framing and present features as current design facts

* docs: add AWS documentation links and clean up memory system documentation

- Add hyperlink to Amazon Bedrock AgentCore Memory main documentation
- Link to Memory Getting Started Guide for the three memory strategies
- Remove Legacy Pattern Recognition section from documentation (code remains)
- Remove Error Handling and Fallbacks section to focus on core functionality
- Keep implementation details in code while streamlining public documentation

* docs: reorganize memory-system.md to eliminate redundancies

- Merged Memory Tool Architecture and Planning sections into unified section
- Consolidated all namespace/actor_id explanations in architecture section
- Combined pattern recognition and memory capture content
- Created dedicated Agent Memory Integration section with examples
- Removed ~15-20% redundant content while improving clarity
- Improved document structure for better navigation

* style: apply ruff formatting and fix code style issues

- Applied ruff auto-formatting to all Python files
- Fixed 383 style issues automatically
- Remaining issues require manual intervention:
  - 29 ruff errors (bare except, unused variables, etc.)
  - 61 mypy type errors (missing annotations, implicit Optional)
- Verified memory system functionality matches documentation
- Confirmed user personalization working correctly in reports

* docs: make benefits section more succinct in memory-system.md

- Consolidated 12 bullet points into 5 focused benefits
- Removed redundant three-category structure (Users/Teams/Operations)
- Maintained all key value propositions while improving readability
- Reduced section length by ~60% while preserving essential information

* feat: add comprehensive cleanup script with memory deletion

- Added cleanup.sh script to delete all AWS resources (gateway, runtime, memory)
- Integrated memory deletion using bedrock_agentcore MemoryClient
- Added proper error handling and graceful fallbacks
- Updated execution order: servers → gateway → memory → runtime → local files
- Added memory deletion to README.md cleanup instructions
- Includes confirmation prompts and --force option for automation

* fix: preserve .env, .venv, and reports in cleanup script

- Modified cleanup script to only remove AWS-generated configuration files
- Preserved .env files for development continuity
- Preserved .venv directories to avoid reinstalling dependencies
- Preserved reports/ directory containing investigation history
- Files removed: gateway URIs, tokens, agent ARNs, memory IDs only
- Updated documentation to clarify preserved vs removed files

* fix: use correct bedrock-agentcore-control client for gateway operations

- Changed boto3 client from 'bedrock-agentcore' to 'bedrock-agentcore-control'
- Fixes 'list_gateways' method not found error during gateway deletion
- Both gateway and runtime deletion now use the correct control plane client

* docs: add memory system initialization timing guidance

- Added note that memory system takes 10-12 minutes to be ready
- Added steps to check memory status with list command after 10 minutes
- Added instruction to run update command again once memory is ready
- Provides clear workflow for memory system setup and prevents user confusion

* docs: comprehensive documentation update and cleanup

- Remove unused root .env and .env.example files (not referenced by any code)
- Update configuration.md with comprehensive config file documentation
- Add configuration overview table with setup instructions and auto-generation info
- Consolidate specialized-agents.md content into system-components.md
- Update system-components.md with complete AgentCore architecture
- Add detailed sections for AgentCore Runtime, Gateway, and Memory primitives
- Remove cli-reference.md (excessive documentation for limited use)
- Update README.md to reference configuration guide in setup section
- Clean up documentation links and organization

The documentation now provides a clear, consolidated view of the system
architecture and configuration with proper cross-references and setup guidance.

* feat: improve runtime deployment and invocation robustness

- Increase deletion wait time to 150s for agent runtime cleanup
- Add retry logic with exponential backoff for MCP rate limiting (429 errors)
- Add session_id and user_id to agent state for memory retrieval
- Filter out /ping endpoint logs to reduce noise
- Increase boto3 read timeout to 5 minutes for long-running operations
- Add clear error messages for agent name conflicts
- Update README to clarify virtual environment requirement for scripts
- Fix session ID generation to meet 33+ character requirement

These changes improve reliability when deploying and invoking agents,
especially under heavy load or with complex queries that take time.

* chore: remove accidentally committed reports folder

Removed 130+ markdown report files from the reports/ directory that were
accidentally committed. The .gitignore already includes reports/ to prevent
future commits of these generated files.

2025-08-06 17:49:56 -04:00

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Configuration

This document provides a comprehensive overview of all configuration files used in the SRE Agent system. Configuration files are organized across different directories based on their purpose and scope.

Configuration Files Overview

File Path	Type	Purpose	Manual Edit Required?	Auto-Generated?
`sre_agent/.env`	ENV	SRE agent-specific settings	Yes	Yes (GATEWAY_ACCESS_TOKEN by setup)
`gateway/.env`	ENV	Gateway authentication settings	Yes	No
`gateway/config.yaml`	YAML	AgentCore Gateway configuration	Yes	Partially (provider_arn by setup)
`deployment/.env`	ENV	Soft link to `sre_agent/.env`	No (uses sre_agent/.env)	N/A (symlink)
`sre_agent/config/agent_config.yaml`	YAML	Agent-to-tool mapping configuration	No	Yes (gateway URI by setup)
`scripts/user_config.yaml`	YAML	Script-specific user configuration	No	No
`backend/openapi_specs/*.yaml`	YAML	OpenAPI specifications for tools	No	Yes (from templates by setup)

Setup Instructions

For files with .example versions:

Copy the .example file to create the actual configuration file
Edit the copied file with your environment-specific values
Never commit the actual configuration files to version control

# Example setup commands
cp sre_agent/.env.example sre_agent/.env
cp gateway/.env.example gateway/.env
cp gateway/config.yaml.example gateway/config.yaml

Files Automatically Updated During Setup

The following files are automatically modified by the setup scripts:

sre_agent/.env - The GATEWAY_ACCESS_TOKEN is automatically appended
sre_agent/config/agent_config.yaml - The gateway.uri field is updated with the created gateway URI
gateway/config.yaml - The provider_arn field is updated when creating the credential provider
backend/openapi_specs/*.yaml - Generated from templates with your backend domain

Environment Variables

The SRE Agent uses environment variables for sensitive configuration values. Create a .env file in the sre_agent/ directory with the following required variables:

# Required: API key for Claude model access
# For Anthropic direct access:
ANTHROPIC_API_KEY=sk-ant-api-key-here

# For Amazon Bedrock access:
AWS_DEFAULT_REGION=us-east-1
AWS_PROFILE=your-profile-name  # Or use AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY

# Required: AgentCore Gateway authentication
GATEWAY_ACCESS_TOKEN=your-gateway-token-here  # Generated by gateway setup

# Optional: Debugging and logging
LOG_LEVEL=INFO  # Options: DEBUG, INFO, WARNING, ERROR
DEBUG=false     # Enable debug mode for verbose output

Note: The SRE Agent looks for the .env file in the sre_agent/ directory, not the project root. This allows for modular configuration management.

Agent Configuration

The agent behavior is configured through sre_agent/config/agent_config.yaml. This file defines the mapping between agents and their available tools, as well as LLM parameters:

# Agent to tool mapping
agents:
  kubernetes_agent:
    name: "Kubernetes Infrastructure Agent"
    description: "Specializes in Kubernetes operations and troubleshooting"
    tools:
      - get_pod_status
      - get_deployment_status
      - get_cluster_events
      - get_resource_usage
      - get_node_status

  logs_agent:
    name: "Application Logs Agent"
    description: "Expert in log analysis and pattern detection"
    tools:
      - search_logs
      - get_error_logs
      - analyze_log_patterns
      - get_recent_logs
      - count_log_events

  metrics_agent:
    name: "Performance Metrics Agent"
    description: "Analyzes performance metrics and trends"
    tools:
      - get_performance_metrics
      - get_error_rates
      - get_resource_metrics
      - get_availability_metrics
      - analyze_trends

  runbooks_agent:
    name: "Operational Runbooks Agent"
    description: "Provides operational procedures and guides"
    tools:
      - search_runbooks
      - get_incident_playbook
      - get_troubleshooting_guide
      - get_escalation_procedures
      - get_common_resolutions

# Global tools available to all agents
global_tools:
  - x-amz-bedrock-agentcore-search  # AgentCore search tool
  
# Gateway configuration
gateway:
  uri: "https://your-gateway-url.com"  # Updated during setup

Gateway Environment Variables

The AgentCore Gateway requires additional environment variables for authentication. Create a .env file in the gateway/ directory with the following:

# Required: Backend API key for credential provider authentication
BACKEND_API_KEY=your-backend-api-key-here

# Optional: Override config.yaml values with environment variables
# ACCOUNT_ID=123456789012
# REGION=us-east-1
# ROLE_NAME=your-role-name
# GATEWAY_NAME=MyAgentCoreGateway
# CREDENTIAL_PROVIDER_NAME=sre-agent-api-key-credential-provider

Note: The BACKEND_API_KEY is used by the create_gateway.sh script to authenticate with the credential provider service.

Gateway Configuration

The AgentCore Gateway is configured through gateway/config.yaml. This configuration is managed by the setup scripts but can be customized:

# AgentCore Gateway Configuration Template
# Copy this file to config.yaml and update with your environment-specific settings

# AWS Configuration
account_id: "YOUR_ACCOUNT_ID"
region: "us-east-1"
role_name: "YOUR_ROLE_NAME"
endpoint_url: "https://bedrock-agentcore-control.us-east-1.amazonaws.com"
credential_provider_endpoint_url: "https://us-east-1.prod.agent-credential-provider.cognito.aws.dev"

# Cognito Configuration
user_pool_id: "YOUR_USER_POOL_ID"
client_id: "YOUR_CLIENT_ID"

# S3 Configuration
s3_bucket: "your-agentcore-schemas-bucket"
s3_path_prefix: "devops-multiagent-demo"  # Path prefix for OpenAPI schema files

# Provider Configuration
# This ARN is automatically generated by create_gateway.sh when it runs create_credentials_provider.py
provider_arn: "arn:aws:bedrock-agentcore:REGION:ACCOUNT_ID:token-vault/default/apikeycredentialprovider/YOUR_PROVIDER_NAME"

# Gateway Configuration
gateway_name: "MyAgentCoreGateway"
gateway_description: "AgentCore Gateway for API Integration"

# Target Configuration
target_description: "S3 target for OpenAPI schema"

Configuration File Details

SRE Agent `.env` File

Location: sre_agent/.env
Purpose: Agent-specific configuration separate from deployment settings
Setup: Copy from sre_agent/.env.example and customize
Auto-Updates: The setup script automatically adds GATEWAY_ACCESS_TOKEN to this file
Note: The agent looks for this file specifically in the sre_agent/ directory

Gateway `.env` File

Location: gateway/.env
Purpose: Gateway authentication and backend API configuration
Setup: Copy from gateway/.env.example and customize
Key Variables: Backend API key for credential provider authentication

Deployment `.env` File

Location: deployment/.env
Purpose: Symbolic link to sre_agent/.env
Setup: No manual setup required - this is a soft link
Note: This symlink ensures deployment scripts use the same environment variables as the agent

Gateway Configuration (`config.yaml`)

Location: gateway/config.yaml
Purpose: AgentCore Gateway settings including AWS, Cognito, and S3 configuration
Setup: Copy from config.yaml.example and customize
Auto-Updates: The create_gateway.sh script automatically updates certain fields like provider_arn

Agent Configuration (`agent_config.yaml`)

Location: sre_agent/config/agent_config.yaml
Purpose: Defines agent-to-tool mappings and agent capabilities
Setup: Edit directly (no example file)
Auto-Updates: The setup script automatically updates the gateway.uri field with the created gateway URI
Content: Agent definitions, tool assignments, and global tool configurations

User Configuration File

Location: scripts/user_config.yaml
Purpose: User personas and preferences for memory-enhanced personalization
Setup: Edit directly to add or modify user personas
Content: Predefined user preferences (Alice: technical, Carol: executive)

OpenAPI Specifications

Location: backend/openapi_specs/*.yaml
Purpose: Define the API contracts for various backend services
Files:
- k8s_api.yaml - Kubernetes operations API
- logs_api.yaml - Log analysis API
- metrics_api.yaml - Metrics collection API
- runbooks_api.yaml - Runbook management API
Auto-Generation: These files are generated from templates during setup when you run generate_specs.sh
Note: Do not edit these directly - modify the templates instead

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