Leadership Overview | Comprehensive Framework | 7-Layer Architecture
Enterprises pursuing agentic AI face critical gaps that prevent successful adoption:
- Fragmented capabilities - AI tools scattered across multiple platforms without unified governance
- Missing enterprise requirements - No clear workspace isolation, model governance, or cost control
- Integration complexity - Difficult to connect AI agents to existing systems and workflows
- Lack of lifecycle management - No CI/CD pipelines or version control for agent definitions
- Unclear ROI - No cost attribution across business units and use cases
We deliver a comprehensive agentic AI framework that brings together all the capabilities, context, integrations, and governance needed to meet your RFP/RFQ requirements.
One Unified Framework. Complete Integration. Enterprise-Grade Governance.
This is not just another AI tool—it's a complete thin client framework deployed in your AWS environment that unifies:
Complete Capability Coverage
- Visual and code-based agent development (flexible interfaces for any user)
- Full document processing, integration, and orchestration capabilities
- Unified knowledge management with RAG and vector databases
- Enterprise-grade security, compliance, and observability
- Comprehensive cost tracking and FinOps
Enterprise-Grade Architecture
- 7-layer modular architecture providing clear separation of concerns
- Declarative YAML-based agent definitions with full CI/CD support
- Git-based workflows with automated testing and environment promotion
- Project-level workspace isolation with resource quotas and budgets
- Model governance with approval workflows (dev → qa → prod)
Seamless Integration
- Connects to your existing systems (ServiceNow, email, document repositories)
- MCP protocol support for external integrations
- Pre-built connectors and custom tool development
- Event-driven architecture with webhooks and streaming
- ✅ Complete Framework: All necessary capabilities unified in one platform—no need to stitch together multiple products
- ✅ YAML-Based Agent Definitions: Declarative, version-controlled agents enabling reproducibility and full CI/CD
- ✅ Project-Level Workspace Isolation: Clear boundaries with independent budgets, quotas, and governance
- ✅ Model Governance: Comprehensive lifecycle management with approval workflows across environments
- ✅ Enterprise Integration: Brings together all context and connections your agents need
- ✅ Deploy in Your AWS: Complete control and security within your environment (BYOA)
Bring Your Own AWS (BYOA)
- Deploy our complete framework in your AWS account
- We provide all infrastructure code, agents, templates, and configurations
- Integrates with your enterprise systems and identity providers
- Full control, security, and compliance within your environment
Our platform directly addresses all key enterprise agentic AI requirements:
| Requirement | How We Deliver | Where |
|---|---|---|
| Visual workflow builder (no-code) | Drag-and-drop interface with pre-built templates | Layer 2 |
| Code-based development | YAML editor, notebooks, full IDE | Layer 2 |
| Agent testing and validation | Test mode, schema validation, integration tests | Layer 2 |
| Template library | 50+ pre-built workflows for common use cases | Layer 2 |
| Multi-agent orchestration | LangGraph, Step Functions for complex workflows | Layer 3 |
| Requirement | How We Deliver | Where |
|---|---|---|
| Multi-model support | Amazon Nova, Claude, Mistral, Llama via Bedrock + AgentCore | Layer 3 |
| Intelligent routing | Cost, performance, quality-based model selection | Layer 3 |
| Real-time response | Sub-second latency with AgentCore + auto-scaling | Layer 3 |
| State management | Bedrock AgentCore Memory (short + long-term) | Layer 3 & 5 |
| Workflow orchestration | Bedrock Flows, Step Functions (AI-native), LangGraph | Layer 3 |
| Multi-agent collaboration | Bedrock Multi-Agent (supervisor + specialized agents) | Layer 3 |
| Requirement | How We Deliver | Where |
|---|---|---|
| Document processing | Textract, Comprehend, custom extractors for PDFs/images | Layer 4 & 5 |
| RAG (Retrieval Augmented Generation) | Bedrock Knowledge Bases + S3 Vectors/Neptune Analytics | Layer 5 |
| Email interpretation | NLP extraction, classification, routing | Layer 4 |
| System integrations | AgentCore Gateway with MCP + pre-built connectors | Layer 4 |
| APIs, webhooks, events | REST APIs, EventBridge, SQS | Layer 4 |
| Knowledge base sync | Automatic synchronization with source systems | Layer 5 |
| Vector embeddings | Amazon Titan Text Embeddings V2 (100+ languages, 33% cost reduction) | Layer 5 |
| Requirement | How We Deliver | Where |
|---|---|---|
| Workspace isolation | Project-level boundaries with independent resources | Layer 6 |
| Multi-tenant capabilities | Complete isolation, RBAC, resource quotas | Layer 6 |
| Model governance | Approval workflows, lifecycle management (dev→qa→prod) | Layer 6 |
| CI/CD pipelines | Git-based workflows with automated testing | Layer 6 |
| Human-in-the-loop (HITL) | Configurable review gates and approval chains | Layer 6 |
| Security & compliance | End-to-end encryption, audit logging, policy engine | Layer 6 |
| YAML-based agents | Declarative, version-controlled configurations | Layer 6 |
| Requirement | How We Deliver | Where |
|---|---|---|
| Observability | CloudWatch AI Observability + X-Ray distributed tracing | Layer 7 |
| FinOps & cost tracking | Real-time cost by workspace, budget alerts | Layer 7 |
| Performance monitoring | Token usage, latency, error rates via CloudWatch AI | Layer 7 |
| Anomaly detection | ML-based detection for cost, errors, performance | Layer 7 |
| Scalability | Auto-scaling with AgentCore + Kubernetes (EKS) | Layer 3 & 7 |
| Lifecycle management | Environment promotion, versioning, rollback | Layer 7 |
Complete Coverage: Every requirement in your RFP/RFQ is addressed by specific components in our 7-layer architecture.
╔═══════════════════════════════════════════════════════════════════════╗
║ ENTERPRISE AGENTIC AI FRAMEWORK ║
║ 7-Layer Architecture ║
╚═══════════════════════════════════════════════════════════════════════╝
┌─────────────────────────────────────────────────────────────────────┐
│ [1] EXPERIENCE LAYER │
│ - Web portal and dashboards │
│ - Workspace selector (choose your project) │
│ - Agent catalog and marketplace │
│ - Monitoring: runs, exceptions, approvals │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [2] BUILDER LAYER (WHERE AGENTS ARE CREATED) │
│ - Amazon SageMaker Unified Studio (GA March 2025) │
│ - Amazon Bedrock IDE + Amazon Q Developer │
│ - Visual builder (Bedrock Flows) & notebooks │
│ - YAML editor, template library, Git integration │
│ - Outputs: YAML agent definitions │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [3] RUNTIME LAYER (WHERE AGENTS EXECUTE) │
│ - Amazon Bedrock AgentCore (serverless agent runtime) │
│ - Foundation models (Amazon Nova, Claude, Mistral, Llama) │
│ - Multi-agent collaboration (supervisor pattern) │
│ - Orchestration (Bedrock Flows, Step Functions AI-native) │
│ - AgentCore Memory (short + long-term) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [4] INTEGRATION & TOOLS LAYER │
│ - Amazon Bedrock AgentCore Gateway (MCP tool server) │
│ - Zero-code MCP tool creation (from APIs/Lambda) │
│ - 200+ MCP connectors (ServiceNow, email, CRM) │
│ - AWS services (Textract, Comprehend, Athena) │
│ - Custom tools (containers on EKS, MCP servers) │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [5] KNOWLEDGE & MEMORY LAYER │
│ - Amazon Bedrock Knowledge Bases (managed RAG) │
│ - Amazon S3 Vectors (90% cost reduction) │
│ - Titan Text Embeddings V2, GraphRAG (Neptune Analytics) │
│ - AgentCore Memory (managed short + long-term memory) │
│ - Multi-tenant isolation per workspace │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [6] GOVERNANCE & CONTROL LAYER │
│ - WORKSPACE ISOLATION (project-level boundaries) │
│ - Multi-tenancy (namespace, resource quotas, budgets) │
│ - YAML-based agents with CI/CD │
│ - Model governance (approval workflows) │
│ - Policy engine (OPA), guardrails, HITL │
│ - IAM/RBAC, audit trails, compliance │
└─────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ [7] PLATFORM OPERATIONS LAYER │
│ - CloudWatch AI Observability (GA Oct 2025, purpose-built) │
│ - Automatic instrumentation (ADOT, zero code changes) │
│ - Token usage tracking, distributed tracing (X-Ray) │
│ - FinOps (cost tracking per workspace, budget alerts) │
│ - Lifecycle management (dev → qa → prod) │
└─────────────────────────────────────────────────────────────────────┘
Purpose: Unified web portal for accessing all platform capabilities
Components:
- Web portal: Single entry point with SSO/SAML authentication
- Workspace selector: Choose project context (e.g., "Document Processing", "Email Triage")
- Agent catalog: Browse, search, and deploy pre-built agents and templates
- Monitoring dashboards: Real-time view of agent runs, exceptions, and approvals
- Role-based access: Tailored views based on user permissions
Key Features:
- Agent marketplace - Discover and deploy pre-built agents
- Workflow monitoring - Track agent executions, success rates, errors
- Human-in-the-loop queues - Review and approve agent decisions
- Cost visibility - Per-workspace budget tracking and usage metrics
- Audit and compliance views - Complete activity logs and traces
User Experience:
Login (SSO) → Select Workspace → Access Capabilities
Example:
User logs in → Selects "Document Processing" workspace
→ Can: Build agents, monitor runs, review approvals
→ All activities scoped to workspace
→ Costs tracked to workspace budget
Technology Stack: Web portal, identity provider integration (SSO/SAML), API Gateway, role-based access control
Purpose: Unified development environment where agents are designed, prototyped, and tested
Amazon SageMaker Unified Studio (GA March 2025):
The platform leverages Amazon SageMaker Unified Studio, AWS's next-generation unified data and AI development environment that converges analytics and generative AI into a single platform.
Integrated Development Capabilities:
- Amazon Bedrock IDE - Purpose-built for GenAI application development with integrated Knowledge Bases, Guardrails, Agents, and Flows
- Jupyter Notebooks - Seamless multi-cluster support for prototyping and experimentation
- Amazon Q Developer - Natural language code generation and task acceleration
- SageMaker Data Agent (Nov 2025) - Generates SQL and Python code from natural language for data automation
- SQL Editor - Query diverse data sources with unified access
- Git Integration - Built-in version control, branching, pull requests
Flexible Interfaces:
Users can build agents using their preferred approach:
- Visual workflow builder - Amazon Bedrock Flows with drag-and-drop nodes (no coding required)
- Code/YAML editor - Full IDE with schema validation, autocomplete, and syntax highlighting
- Notebooks - Jupyter notebooks for prototyping with LangChain, LangGraph, LlamaIndex
- All approaches output YAML-based agent definitions for consistent deployment
Core Capabilities:
- Template library - 50+ pre-built workflows for common use cases
- YAML editor - Schema validation, autocomplete, and syntax highlighting
- Testing framework - Test mode with sample data, integration tests
- Custom tool development - Python functions deployed as containerized services or MCP servers
- Model fine-tuning - Integrated model training and customization
- Data catalog access - SageMaker Catalog for secure data discovery and governance
Output Artifacts:
- Agent definitions - YAML format with all configuration
- Workflow logic - Bedrock Flows, LangGraph definitions, Step Functions state machines
- Custom tools - Containerized services (EKS) or MCP servers (AgentCore Gateway)
- All version-controlled - Git-based workflows with full history
Key Features:
- Declarative YAML-based agent configs (primary artifact format)
- Schema validation before deployment
- Template-driven development for faster time-to-value (up to 40% reduction)
- Full collaboration across data engineers, analysts, and AI developers
- Integrated data access via SageMaker Lakehouse
Technology Stack:
- Primary IDE: Amazon SageMaker Unified Studio with Bedrock IDE
- Visual workflows: Amazon Bedrock Flows
- Frameworks: LangChain, LangGraph, CrewAI, LlamaIndex
- AI Assistant: Amazon Q Developer
- Deployment: Kubernetes (EKS), container registry, Git repositories
Purpose: Production environment where deployed agents execute at scale
Amazon Bedrock AgentCore (Production Runtime Platform):
Amazon Bedrock AgentCore is the fully managed, serverless hosting platform for deploying and operating AI agents at enterprise scale. It provides purpose-built runtime infrastructure separate from agent development.
AgentCore Capabilities:
- Extended Sessions: Up to 8-hour long-running workloads for complex multi-step agent tasks
- Framework Agnostic: Works with LangGraph, CrewAI, LlamaIndex, LangChain
- Model Agnostic: Supports Amazon Nova, Claude, Mistral, Llama, and custom models
- Session Isolation: Dedicated microVMs per user session for data protection and state management
- MCP Protocol Support: Model Context Protocol for agent-to-agent and agent-to-tool communication
- Deployment Options: Code-zip upload for rapid prototyping OR container-based for advanced configs
Foundation Model Access via Amazon Bedrock:
- Amazon Nova family (Dec 2024): Frontier intelligence at 75% lower cost
- Nova Micro: Text-only, ultra-low latency (128K context)
- Nova Lite: Multimodal, lightning fast (300K context)
- Nova Pro: Best accuracy/speed/cost for agentic workflows (300K context)
- Claude (Anthropic): Advanced reasoning and tool use
- Mistral: High-performance European models
- Llama: Open-source flexibility
- Custom models: Import and deploy proprietary LLMs
Extensibility: The platform architecture is designed for AWS Bedrock models as the primary foundation. Future phases can extend to additional model providers through the abstraction layer, enabling multi-cloud model access while maintaining consistent governance and operations.
Intelligent Routing & Management:
- Cost-based routing: Route to Nova Micro for simple tasks, Nova Pro for complex reasoning
- Performance-based: Sub-second response times with optimal model selection
- Quality-based: Route based on task requirements and accuracy needs
- Rate limiting: Quota management and cost controls per workspace
Agent Orchestration:
- Amazon Bedrock Agents: Multi-agent collaboration with supervisor pattern (GA March 2025)
- Amazon Bedrock Flows: Visual workflow builder with 24-hour async execution (GA Nov 2024)
- AWS Step Functions: AI-native orchestration combining rule-based + agent-driven reasoning
- LangGraph: Complex cyclic workflows on AgentCore runtime
- Event-driven: EventBridge triggers, webhooks, SQS for batch processing
State & Memory Management:
- Amazon Bedrock AgentCore Memory (2025): Managed short-term and long-term memory
- Session state: Maintained across multi-turn conversations
- Retry and error handling: Built-in resilience patterns
Key Features:
- Enterprise-grade security with dedicated session isolation
- Automatic scaling with AgentCore's serverless infrastructure
- Sub-second response times for urgent queries
- Extended 8-hour sessions for complex reasoning tasks
- Graceful degradation and fallback strategies
Technology Stack:
- Production Runtime: Amazon Bedrock AgentCore
- Agent Orchestration: Amazon Bedrock Agents (multi-agent), Bedrock Flows (visual)
- Foundation Models: Amazon Bedrock (Nova, Claude, Mistral, Llama)
- Workflow: AWS Step Functions (AI-native), LangGraph, EventBridge, SQS
- Memory: Amazon Bedrock AgentCore Memory
Purpose: Agent capabilities—what agents can do and what they can access
Amazon Bedrock AgentCore Gateway (Centralized Tool Server):
AgentCore Gateway provides a unified interface for agents to discover, access, and invoke tools across the enterprise.
Gateway Capabilities:
- Zero-Code MCP Tool Creation: Automatically generate MCP tools from APIs and Lambda functions
- Intelligent Tool Discovery: Agents can discover and invoke available tools dynamically
- Unified Tool Interface: Standardized access to internal and external tools
- Built-in Authorization: Inbound and outbound access controls
- Serverless Infrastructure: Fully managed MCP server hosting
Native AWS Tools:
- Document Intelligence: Textract (OCR), Comprehend (NLP, PII detection), Rekognion (images)
- Data Processing: Athena (SQL queries), Glue (ETL workflows)
- Communication: SES (email), SNS (notifications), EventBridge (events)
- Storage & Databases: S3, DynamoDB, RDS, Aurora
Enterprise Integration via MCP:
- Pre-built MCP connectors: ServiceNow, Exchange/Outlook, Salesforce, Workday, Slack
- API Integration: REST, GraphQL, webhooks exposed as MCP tools
- Agent-to-Agent Communication: MCP protocol enables multi-agent coordination
- Data Access Controls: IAM-based permissions with comprehensive audit logging
Custom Tool Development:
- In SageMaker Unified Studio: Write Python functions with natural language via Amazon Q Developer
- Deployment Options:
- MCP Servers: Deploy via AgentCore Gateway (zero-code generation from Lambda/APIs)
- Containerized Services: Deploy on EKS for complex custom logic
- Auto-Registration: Tools automatically available in AgentCore Gateway catalog
- Versioning: Track tool versions with rollback capability
Advanced Capabilities (2025):
- Computer Use (Beta): Agents interact with computer systems programmatically
- Memory Tool (Beta): Claude Sonnet 4.5 retrieves information beyond context window
- Fine-Grained Streaming: Real-time tool call streaming for responsive interactions
Key Features:
- 200+ pre-built MCP connectors via AgentCore Gateway
- Zero-code tool creation from existing APIs/Lambda functions
- Full MCP protocol support for agent-to-agent and agent-to-tool communication
- Multi-modal data support (text, images, PDFs, audio, video)
- Intelligent tool routing based on agent needs
Technology Stack:
- Tool Server: Amazon Bedrock AgentCore Gateway (MCP)
- Custom Tools: Containerized services (EKS), Lambda functions
- Connectors: AWS AppFlow (SaaS), API Gateway, EventBridge
- Security: Secrets Manager, IAM, audit logging
Purpose: Agent knowledge, conversation history, and contextual understanding
Amazon Bedrock Knowledge Bases (Managed RAG):
Fully managed RAG (Retrieval Augmented Generation) with complete workflow from data ingestion to retrieval to prompt augmentation.
Core RAG Capabilities:
- In-built session context management with automatic source attribution
- Advanced chunking strategies: Semantic, hierarchical, fixed-size, or custom Lambda-based
- Hybrid retrieval: Semantic + keyword search for comprehensive information gathering
- GraphRAG Integration: Neptune Analytics auto-creates embeddings and relationship graphs for improved retrieval accuracy
- Metadata filtering: Filter by document type, date, department, custom business attributes
- Multi-tenant isolation: Separate indices per workspace
Vector Storage Options (2025):
- Amazon S3 Vectors (July 2025, Preview): Native vector storage and querying in S3 with 90% cost reduction
- Amazon Neptune Analytics: Graph-based vector storage with relationship understanding
- Amazon OpenSearch Serverless: Managed vector database with search capabilities
- MongoDB, Pinecone, Redis Enterprise Cloud: Third-party integrations
Embeddings:
- Amazon Titan Text Embeddings V2 (April 2024):
- Flexible output sizes: 256, 512, 1,024 dimensions (vs. 1,536 in V1)
- 33% cost reduction with 99% accuracy at 512 dims
- 100+ language support with 8,192 token capacity
- Optimized for RAG, Q&A, classification, recommendations
- Binary Vector Embeddings (Nov 2024): Titan V2 and Cohere Embed support for reduced storage/compute
- Amazon Nova Multimodal Embeddings (2025): Unified embeddings for text, documents, images, video, audio
Document Processing:
- Ingestion pipeline: S3 → chunking → embedding → indexing (fully automated)
- Supported formats: PDFs, images, scans, emails, Office documents, videos
- Synchronization: Automatic updates from source systems
- LangChain/LlamaIndex integration: Use existing RAG pipelines
Amazon Bedrock AgentCore Memory:
Managed service for context-aware agent memory (announced AWS Summit NYC 2025).
Memory Management:
- Short-Term Memory: Captures immediate conversation context within active sessions
- Long-Term Memory: Stores persistent insights, preferences, and learnings across sessions
- Automatic Context Management: No manual memory infrastructure required
- Full Control: Developers specify what agents remember and for how long
- Cross-Session Continuity: Seamless experience as agents recall previous interactions
Alternative Memory Options:
- Bedrock Agents Memory Retention (Preview July 2024): Built-in memory for Bedrock-native agents
- Custom State Management: DynamoDB for flexible custom memory implementations
Conversation Capabilities:
- Session summarization: Condense long conversations for context efficiency
- Entity tracking: Track key entities (people, amounts, dates) across turns
- Preference learning: Remember user preferences and behaviors over time
Key Features:
- Fully managed RAG eliminating infrastructure complexity
- 90% cost savings with S3 Vectors
- Hybrid retrieval combining semantic understanding + keyword precision
- GraphRAG for relationship-aware retrieval
- Managed short + long-term memory via AgentCore Memory
- Multi-modal embeddings (text, images, video, audio)
Technology Stack:
- RAG Platform: Amazon Bedrock Knowledge Bases
- Vector Storage: Amazon S3 Vectors, Neptune Analytics, OpenSearch Serverless
- Embeddings: Amazon Titan Text Embeddings V2, Nova Multimodal Embeddings
- Memory: Amazon Bedrock AgentCore Memory
- Custom State: DynamoDB (for advanced use cases)
Purpose: Enterprise-grade security, compliance, and workspace boundaries
Components:
Workspace Isolation (Project-Level)
- Each workspace = isolated project (e.g., "Document Processing", "Email Triage")
- Any user can access any workspace (with appropriate permissions)
- Complete development environment available in every workspace
- Resource boundaries: S3 prefix
/workspace-{id}/, agentsworkspace-{id}-* - Cost tracking per workspace
- Resource quotas: Max agents, monthly budget, API rate limits
Example Workspace Structure:
Organization
│
├─ Workspace 1: "Document Processing"
│ ├─ Users: 15 users with various roles
│ ├─ Agents: 10 YAML-defined agents deployed on AgentCore
│ ├─ Resources: S3 /workspace-1/, agents workspace-1-*
│ └─ Budget: $5,000/month
│
├─ Workspace 2: "Email Triage"
│ ├─ Users: 8 users with various roles
│ ├─ Agents: 5 YAML-defined agents deployed on AgentCore
│ ├─ Resources: S3 /workspace-2/, agents workspace-2-*
│ └─ Budget: $2,000/month
Multi-Tenancy
- Namespace-level isolation (IAM policies, resource tags)
- Cost allocation tags (WorkspaceID, CostCenter)
- Cross-workspace analytics with privacy preservation
YAML-Based Agents with CI/CD
- Declarative agent configurations
- Git-based workflows (commit → test → deploy)
- Automated validation (schema, policy, security)
- Environment promotion (dev → qa → prod)
- Approval gates before production
Model Governance
- Model registry for all models (base, fine-tuned, custom)
- Approval workflows for production deployment
- Lifecycle management (dev → qa → prod)
- Usage tracking and cost attribution
Security & Compliance
- IAM roles with least privilege
- SSO/MFA integration (SAML, OIDC)
- End-to-end encryption (KMS-managed keys)
- Comprehensive audit logging (CloudTrail)
- PII detection and redaction (Macie, Comprehend)
- Policy enforcement (OPA, Bedrock Guardrails)
Key Features:
- Project-level workspace isolation
- YAML-based declarative agents
- Git-based CI/CD pipelines
- Model governance with approvals
- Full audit trail
- Compliance-ready (SOC 2, GDPR, HIPAA, FINRA)
AWS Services: IAM, KMS, Secrets Manager, Cognito, CloudTrail, Macie, GuardDuty, CodePipeline
Purpose: Comprehensive monitoring, cost tracking, and operational excellence
Components:
Amazon CloudWatch Generative AI Observability (GA October 2025):
Purpose-built observability for AI applications and agents with comprehensive monitoring across all components.
AI-Specific Monitoring:
- Token Usage Tracking: Real-time monitoring of token consumption across all models and agents
- Latency Metrics: End-to-end latency tracking (p50, p95, p99) for agent responses
- Model inference time
- Tool invocation duration
- Knowledge base retrieval latency
- Complete agent workflow timing
- Error Detection: AI-specific error tracking and failure pattern analysis
- Agent Operation Performance: Monitor agent decision quality, escalation rates, confidence scores
Framework Support:
- Automatic Instrumentation: AWS Distro for OpenTelemetry (ADOT) SDK with zero code changes
- Native Integration: Strands Agents, LangChain, LangGraph support
- AgentCore Integration: Built-in observability for:
- AgentCore runtime operations
- Gateway tool invocations
- Memory operations (short + long-term)
- Identity and access patterns
Distributed Tracing:
- X-Ray Integration: End-to-end traces for all agent executions
- Complete Visibility: API call → orchestration → LLM call → tool invocation → knowledge retrieval → response
- Rich Metadata: Workspace ID, agent ID, model used, cost per request, confidence scores, user identity
Out-of-the-Box Dashboards:
- Pre-built AI Dashboards: No configuration required
- Workspace-Level Views: Custom dashboards per project
- Real-Time Monitoring: Live agent performance and health status
- Component-Level Breakdown: Drill down into specific tools, models, or knowledge bases
Intelligent Anomaly Detection:
- ML-Based Detection: CloudWatch Insights automatically identifies unusual patterns
- AI-Specific Alerts:
- Token usage spikes indicating inefficient prompts
- Error rate increases by model or agent
- Latency degradation in specific components
- Cost anomalies by workspace
- Proactive Issue Detection: Identify problems before user impact
Logging & Audit:
- Structured Logging: CloudWatch Logs with JSON format
- PII Detection & Redaction: Automatic sensitive data protection (Amazon Comprehend integration)
- Immutable Audit Trail: S3 storage with 7-year retention for compliance
- Full Traceability: Every agent decision, tool call, and data access logged
FinOps
Cost Tracking
- Real-time cost by workspace, agent, use case
- Token-level tracking for LLM costs
- Resource usage: Lambda, storage, data transfer
Budget Management
- Per-workspace budget quotas
- Alerts at 80%, hard limits at 100%
- Optimization recommendations (model substitution, caching)
Chargeback
- Monthly cost reports by business unit
- Per-agent cost breakdown
- Export to CSV/PDF for billing
Lifecycle Management
- Environment promotion (dev → qa → prod)
- Versioning and rollback
- Deprecation workflows
- Configuration management via Infrastructure as Code
Key Features:
- Purpose-built AI observability (GA October 2025)
- Automatic instrumentation with zero code changes
- End-to-end distributed tracing with X-Ray
- Real-time cost visibility per workspace
- Budget alerts and quota enforcement
- ML-based anomaly detection for AI workloads
- Out-of-the-box dashboards
Technology Stack:
- AI Observability: Amazon CloudWatch Generative AI Observability
- Instrumentation: AWS Distro for OpenTelemetry (ADOT)
- Tracing: AWS X-Ray
- Cost Management: AWS Cost Explorer, AWS Budgets
- Audit: CloudWatch Logs, S3 (immutable storage)
Our 7-layer architecture delivers comprehensive agentic AI capabilities mapped to industry-standard capability categories:
- ✓ Visual workflow builder (drag-and-drop, no-code) → Layer 2
- ✓ Code-based development (YAML, Python, notebooks) → Layer 2
- ✓ Template library with 50+ pre-built workflows → Layer 2
- ✓ Prompt engineering studio → Layer 2
- ✓ Multi-agent orchestration → Layer 3
- ✓ Agent testing and validation → Layer 2
- ✓ Amazon Bedrock AgentCore (production agent runtime) → Layer 3
- ✓ Multi-model support (Amazon Nova, Claude, Mistral, Llama) → Layer 3
- ✓ Intelligent routing (cost, performance, quality-based) → Layer 3
- ✓ State management (AgentCore Memory: short + long-term) → Layer 3
- ✓ Multi-agent collaboration (supervisor + specialized agents) → Layer 3
- ✓ Workflow orchestration (Bedrock Flows, Step Functions AI-native, LangGraph) → Layer 3
- ✓ Auto-scaling and serverless infrastructure (AgentCore) → Layer 3
- ✓ Bedrock Knowledge Bases (managed RAG) → Layer 5
- ✓ Amazon S3 Vectors (90% cost reduction) → Layer 5
- ✓ GraphRAG (relationship-aware retrieval via Neptune Analytics) → Layer 5
- ✓ Titan Text Embeddings V2 (33% cost reduction, 100+ languages) → Layer 5
- ✓ Document processing (PDFs, images, emails, videos) → Layer 5
- ✓ Hybrid retrieval (semantic + keyword search) → Layer 5
- ✓ AgentCore Memory (managed short + long-term memory) → Layer 5
- ✓ Knowledge base synchronization → Layer 5
- ✓ Amazon Bedrock AgentCore Gateway (centralized tool server) → Layer 4
- ✓ Zero-code MCP tool creation (from APIs/Lambda) → Layer 4
- ✓ 200+ pre-built MCP connectors (ServiceNow, email, CRM) → Layer 4
- ✓ Custom tool development (containerized services, MCP servers) → Layer 4
- ✓ Full MCP protocol support → Layer 4
- ✓ APIs, webhooks, event streaming → Layer 4
- ✓ Enterprise system integration → Layer 4
- ✓ Multi-modal data support (text, images, documents, audio, video) → Layer 4
- ✓ Workspace isolation (project-level boundaries) → Layer 6
- ✓ Model governance with approval workflows → Layer 6
- ✓ Policy engine (OPA) and guardrails → Layer 6
- ✓ RBAC, encryption, audit trails → Layer 6
- ✓ CI/CD pipelines with automated testing → Layer 6
- ✓ YAML-based agent definitions (version-controlled) → Layer 6
- ✓ Human-in-the-loop workflows → Layer 6
- ✓ Compliance (SOC 2, GDPR, HIPAA, FINRA) → Layer 6
- ✓ CloudWatch AI Observability (GA Oct 2025, purpose-built for AI) → Layer 7
- ✓ Automatic instrumentation (ADOT, zero code changes) → Layer 7
- ✓ Token usage tracking and cost monitoring → Layer 7
- ✓ Distributed tracing (X-Ray integration) → Layer 7
- ✓ FinOps (cost tracking, budget alerts, chargeback) → Layer 7
- ✓ ML-based anomaly detection (AI-specific) → Layer 7
- ✓ Out-of-the-box dashboards → Layer 7
- ✓ Lifecycle management (dev → qa → prod) → Layer 7
A key differentiator of our framework is declarative agent definitions using YAML —inspired by modern data platforms like Snowflake and dbt that revolutionized data engineering through version-controlled, testable configurations.
Traditional agentic AI platforms require agents to be built through point-and-click UIs or programmatic code, making them:
- Hard to version control - No clear diff between versions
- Difficult to test - Manual testing required for each change
- Impossible to reproduce - "It worked on my machine" problems
- Challenging to govern - No approval workflows or environment promotion
Agents are defined in declarative YAML files that specify:
- Model configuration (which LLM, temperature, parameters)
- Tools and integrations the agent can access
- Workflow orchestration logic
- Human-in-the-loop review conditions
- Governance requirements (approvals, testing)
- ✅ Declarative - Describe what you want, not how to build it
- ✅ Version Controlled - Git-friendly, reviewable, diff-able
- ✅ Reproducible - Exact agent recreation from YAML across environments
- ✅ Testable - Validate schema, policy, and behavior before deployment
- ✅ CI/CD Friendly - Automated pipelines for testing and deployment
- ✅ Human Readable - Business stakeholders can review and understand configurations
agent:
name: "document-processor-v3"
version: "3.0.0"
workspace: "document-processing"
description: "Extracts entities from investment management agreements using Amazon Nova"
# Runtime Configuration
runtime:
platform: "bedrock-agentcore" # Deploy on Amazon Bedrock AgentCore
session_timeout: 3600 # 1-hour session (max 8 hours)
deployment_type: "container" # or "code-zip" for rapid prototyping
# Foundation Model Selection
model:
provider: "bedrock"
model_id: "amazon.nova-pro-v1" # Amazon Nova Pro for agentic workflows
temperature: 0.2
max_tokens: 4096
fallback_model: "anthropic.claude-3-sonnet" # Fallback if Nova unavailable
# Tools accessed via AgentCore Gateway
tools:
gateway: "agentcore-gateway"
mcp_tools:
- textract-ocr # AWS Textract via MCP
- comprehend-ner # AWS Comprehend via MCP
- custom-validator-api # Custom API exposed as MCP tool
custom_containers:
- legal-compliance-checker # Complex logic on EKS
# Workflow Orchestration
workflow:
type: "langgraph" # LangGraph on AgentCore runtime
graph_definition: "./graphs/doc_processing.py"
orchestration:
multi_agent: false
supervisor_agent: null
# Knowledge & Memory
knowledge:
knowledge_base_id: "kb-doc-processing-v2"
provider: "bedrock-kb" # Amazon Bedrock Knowledge Bases
vector_store: "s3-vectors" # Amazon S3 Vectors (90% cost savings)
embedding_model: "amazon.titan-embed-text-v2"
retrieval_strategy: "hybrid" # Semantic + keyword via GraphRAG
memory:
provider: "agentcore-memory" # Amazon Bedrock AgentCore Memory
short_term:
enabled: true
session_duration: 3600 # 1 hour
long_term:
enabled: true
retention_days: 365
store_preferences: true
# Human-in-the-Loop
human_review:
enabled: true
conditions:
- confidence_below: 0.85
- amount_exceeds: 1000000
- entity_type: "legal_covenant"
- keyword_match: ["material adverse change", "force majeure"]
assignee_groups:
- "investment-analysts"
- "legal-review-team"
sla_hours: 24
# Governance & Compliance
governance:
approval_required: true
approvers:
- "data-science-lead"
- "security-team"
- "compliance-officer"
environments:
dev: auto-deploy
qa: requires-approval
prod: requires-approval-and-testing
guardrails:
bedrock_guardrail_id: "gr-compliance-v1"
pii_detection: true
hallucination_check: true # Automated reasoning checks
# Observability
observability:
cloudwatch_ai: true # CloudWatch AI Observability enabled
tracing: "x-ray"
metrics:
- token_usage
- latency_p99
- error_rate
- confidence_scores
alerts:
- type: "cost_anomaly"
threshold: 120% # Alert if 20% over baseline
- type: "error_rate"
threshold: 5%
# Cost Management
finops:
workspace_budget: "workspace-doc-processing-budget"
cost_allocation_tags:
department: "investment-operations"
project: "alternative-investments"
cost_center: "FFIO-ALT-001"Developer commits YAML to Git
↓
Automated Pipeline Triggered
↓
┌─────────────────────┐
│ Stage 1: Validation │
│ • Schema validation │
│ • Policy checks │
│ • Security scan │
│ • Cost estimation │
└─────────┬───────────┘
↓
┌─────────────────────┐
│ Stage 2: Testing │
│ • Integration tests │
│ • Accuracy tests │
│ • Performance tests │
└─────────┬───────────┘
↓
┌─────────────────────┐
│ Stage 3: Deploy Dev │
│ • Auto-deploy │
│ • Register in │
│ agent registry │
└─────────┬───────────┘
↓
┌─────────────────────┐
│ Approval Gate: QA │
│ • Manual review OR │
│ • Auto (low risk) │
└─────────┬───────────┘
↓
Deploy QA → Approval → Deploy Prod
Concept: Workspace = Logical Project Boundary
Each workspace represents an isolated project (e.g., "Document Processing", "Email Triage") with complete separation of:
- Users and teams - RBAC controls who can access what
- Agents and workflows - All agent definitions scoped to workspace
- Resources - Dedicated S3 buckets, databases, vector stores, models
- Budget and cost tracking - Independent cost allocation and quotas
- Governance policies - Workspace-specific approval workflows and policies
- Complete isolation - No cross-workspace data or resource access without explicit sharing
- Independent budgets - Each workspace has its own cost tracking and quota limits
- Flexible access control - Users can access multiple workspaces with appropriate permissions
- Controlled sharing - Approved agents can be shared via marketplace
- Audit trail - All workspace activities logged independently
Organization
│
├─ Workspace: "Document Processing"
│ ├─ Users: 15 users with various roles
│ ├─ Agents: 10 YAML-defined agents
│ ├─ Resources:
│ │ ├─ S3: /workspace-doc-proc/
│ │ ├─ Agents: workspace-doc-proc-*
│ │ └─ Vector DB: doc-proc-knowledge-base
│ ├─ Budget: $5,000/month
│ └─ Quotas: Max 20 agents, 100K API calls/day
│
├─ Workspace: "Email Triage"
│ ├─ Users: 8 users with various roles
│ ├─ Agents: 5 YAML-defined agents
│ ├─ Resources: S3 /workspace-email/, agents workspace-email-*
│ ├─ Budget: $2,000/month
│ └─ Quotas: Max 10 agents, 50K API calls/day
│
├─ Workspace: "Fraud Detection"
│ ├─ Users: 12 users with various roles
│ ├─ Agents: 8 YAML-defined agents
│ ├─ Resources: Dedicated infrastructure with enhanced security
│ ├─ Budget: $8,000/month
│ └─ Quotas: Max 30 agents, 200K API calls/day
- Curated templates - Approved agents/workflows accessible across all workspaces
- Approval required - Security scan, testing, and review before publishing
- Version control - Full version history and rollback capabilities
- Usage tracking - Monitor adoption and performance across workspaces
- Governance - Central approval workflow for marketplace additions
A unified portal where business and technical users can:
- Discover and deploy pre-built agents for common use cases (document processing, email triage, exception handling)
- Browse templates for workflows
- Configure new use cases through intuitive forms and wizards, without writing code
- Monitor agent runs with dashboards showing successes, exceptions, and pending approvals
- Access role-based views tailored to their persona
One front door for AI - Business units don't need to navigate multiple tools or submit tickets to technology
Reusability at scale - Each agent and template becomes a building block for future use cases
Controlled democratization - Technology leadership maintains visibility and governance while enabling self-service
- Agent catalog with search, filtering, and version history
- Pre-built templates for common workflows
- Role-based dashboards and access control
- Real-time monitoring of agent executions
- Human-in-the-loop review queues
Visual workflow designer for creating end-to-end processes:
- Drag-and-drop nodes for AI tasks, business rules, approvals, and system integrations
- Configuration of human-in-the-loop decision points and escalation paths
- Support for both synchronous (real-time) and asynchronous (batch) processing
Agent orchestration that coordinates:
- Multiple AI models working together on complex tasks
- Traditional rule engines alongside AI decision-making
- External system calls and data validation steps
- Conditional branching based on confidence scores, risk thresholds, or business rules
Template library with pre-configured workflows for:
- Document processing (contracts, invoices, agreements)
- Email-to-case routing and automated triage
- Exception handling with analyst review and approval chains
Business process automation, not just chatbots - Enables end-to-end journey automation
Progressive automation - Start with human-in-the-loop, gradually increase autonomy as confidence builds
Flexibility without complexity - Business users configure workflows; technology team defines the building blocks
- Visual drag-and-drop workflow designer
- Pre-built workflow templates
- Human-in-the-loop gates and approval workflows
- Multi-agent orchestration
- Synchronous and asynchronous execution modes
- Version control and rollback
LLM Gateway providing:
- Vendor-agnostic abstraction - Start with AWS Bedrock, seamlessly add other providers later
- Intelligent routing - Route requests to different models based on task complexity, cost, and latency requirements
- Rate limiting and quota management - Prevent runaway usage and cost overruns
Agent execution runtime that:
- Manages agent lifecycle (instantiation, state management, termination)
- Handles retries, fallbacks, and error recovery
- Implements guardrails for safety and compliance
- Supports both stateless (one-shot) and stateful (multi-turn) agent interactions
Performance optimization:
- Caching of common queries and document embeddings
- Parallel processing of independent tasks
- Smart batching to optimize API calls
Cost control and flexibility - Not locked into a single vendor's pricing or capability roadmap
Real-time decisioning - Sub-second response times for urgent queries; intelligent queueing for batch work
Enterprise reliability - Built-in resilience patterns proven in production environments
- LLM gateway (multi-model abstraction)
- Multi-model support (Amazon Nova, Claude, Mistral, Llama)
- Intelligent cost-based routing
- Agent state management (via AgentCore Memory)
- Retry and fallback strategies
- Response caching and optimization
Document processing engine supporting:
- PDFs, images, scans, emails for sources like contracts, agreements, fund documents
- Extraction of key entities (parties, dates, terms, covenants, exceptions)
- Amendments and versioning across documents
- Confidence scoring and validation rules with human review thresholds
Enterprise integration layer with:
- Pre-built connectors to ServiceNow, email systems, existing workflow tools, and document repositories
- API-first architecture supporting REST, webhooks, message queues, and event streaming
- MCP (Model Context Protocol) for external system integration and agent-to-agent communication
- Data access controls ensuring agents only access authorized information
Knowledge management:
- Vector databases for semantic search across policies, playbooks, and historical cases
- Hybrid retrieval (semantic + keyword) for comprehensive information gathering
- Automatic synchronization with source systems to keep knowledge current
Agents have context - Access to the right documents, data, and knowledge to make informed decisions
Leverage existing investments - Doesn't replace current systems; intelligently extends them
Compliance-ready - All data access is logged, authorized, and auditable
- Document processing (Textract, Comprehend, custom extractors)
- Pre-built connectors (ServiceNow, email, CRM, document repositories)
- Vector databases for RAG (Retrieval Augmented Generation)
- Hybrid search (semantic + keyword)
- MCP protocol support
- API-first integration architecture
Multi-tenant architecture with:
- Complete isolation between workspaces (project-level)
- Independent configurations, resource quotas, and data boundaries
- Cross-workspace analytics with privacy preservation
- Role-based access control (RBAC) managing user permissions at workspace level
Security and compliance framework:
- End-to-end encryption (data at rest and in motion)
- Comprehensive audit logging (who did what, when, with which data)
- Policy enforcement aligned with internal standards
- Integration with identity providers (SSO, MFA) and secrets management
Comprehensive observability:
- Structured logging capturing all agent actions, decisions, and data access
- Distributed tracing for end-to-end workflow visibility
- Real-time metrics and dashboards showing success rates, latency, escalation rates
- Anomaly detection flagging unusual patterns in agent behavior or outputs
- Performance profiling identifying bottlenecks and optimization opportunities
Governance controls:
- Model usage policies (approved vs. restricted models)
- Data usage policies (retention, regional restrictions, PII handling)
- Output validation and guardrails for risky or non-compliant responses
Enterprise-grade from day one - Built for regulated industries, not retrofitted
Risk and audit confidence - Full transparency into what AI is doing and why
Operational excellence - Proactive issue detection and resolution before users are impacted
- Multi-tenant isolation (workspace-based)
- End-to-end encryption (KMS-managed keys)
- Comprehensive audit logging (CloudTrail, CloudWatch)
- Distributed tracing (X-Ray)
- Real-time metrics and dashboards
- Anomaly detection and alerting
- Policy engine (OPA) for governance
- SSO/MFA integration
Cost visibility and control:
- Real-time cost tracking by workspace, use case, model, and user
- Budget thresholds and alerts preventing cost overruns
- Cost attribution showing which workflows and agents drive spend
- Optimization recommendations suggesting model substitutions or caching strategies
- FinOps dashboard for technology and finance stakeholders
Resource management:
- Workspace-level quotas (API calls, storage, compute)
- Auto-scaling policies balancing performance and cost
- Subscription and license tracking
Configuration-driven architecture:
- Infrastructure as Code for reproducible deployments
- GitOps workflow with version control for all configurations
- Environment promotion (sandbox → pilot → production) with automated testing
- Automated deployment pipelines with rollback capabilities
Lifecycle management:
- Versioning of agents, workflows, and templates
- Rollback capabilities for failed deployments
- Deprecation and retirement workflows
- Documentation and change tracking
Predictable, sustainable AI adoption - CFO and CIO can confidently invest knowing costs are transparent and controlled
Rapid iteration without chaos - Deploy changes frequently while maintaining stability
Operational maturity - Enterprise-grade change management and disaster recovery from day one
- Real-time cost tracking and attribution
- Budget alerts and quota management
- FinOps dashboard
- Infrastructure as Code
- GitOps workflow with version control
- Environment promotion pipelines
- Agent versioning and rollback
What We Provide:
- Complete agentic AI framework (all 7 layers)
- Infrastructure as Code
- Pre-built agent templates
- Development environment configuration
- Integration connectors
- Documentation and best practices
- Deployment automation scripts
- Support and training
What You Provide:
- AWS account (deployment target)
- Your data, documents, workflows
- Integration credentials (ServiceNow, email, etc.)
- SSO/identity provider details
- Compliance and security requirements
Week 1: Environment Setup
- Day 1-2: AWS account structure (dev, qa, prod)
- Day 3-4: Infrastructure deployment
- Day 5-7: Integration configuration (ServiceNow, email, SSO)
Week 2: Onboarding & First Agents
- Day 1-2: User onboarding and training
- Day 3-4: Deploy pre-built templates
- Day 5-7: Create first custom agents, deploy to production
Timeline: 7-10 days for initial deployment and first production agents
We include templates for common use cases:
Document Processing
- Extract data from contracts, invoices, agreements
- Classify document types
- Validate extracted entities
- Route for human review if needed
Email Triage
- Parse incoming emails
- Classify intent and urgency
- Route to appropriate queue
- Generate draft responses
Exception Handling
- Identify exceptions and anomalies
- Enrich with context from knowledge base
- Route to appropriate analyst
- Track resolution and outcomes
Data Reconciliation
- Compare data across systems
- Identify discrepancies
- Flag for human review
- Update systems of record
Each template includes:
- Pre-configured workflows
- Sample prompts
- Test data
- Deployment guide
- Customization instructions
One Unified Framework. Complete Integration. Enterprise-Grade Governance.
We deliver a comprehensive agentic AI framework that brings together all the capabilities, context, and governance your organization needs—deployed entirely in your AWS environment:
- ✅ Complete Framework: All capabilities unified in one platform—no need to stitch together multiple products
- ✅ YAML-Based Agent Definitions: Declarative, version-controlled, Git-friendly agents with full CI/CD
- ✅ Project-Level Workspace Isolation: Clear boundaries with independent budgets, quotas, and governance
- ✅ Model Governance: Comprehensive lifecycle management with approval workflows (dev → qa → prod)
- ✅ Enterprise Integration: Connects all your systems and brings context together for your agents
- ✅ Deploy in Your AWS (BYOA): Complete control, security, and compliance within your environment
- ✅ Production-Ready from Day One: Security, observability, and FinOps built-in, not bolted on
vs Point Solutions: Competitors offer fragmented tools requiring extensive integration work. We provide a complete, unified framework with all capabilities working together out of the box.
vs Platform-as-a-Service: SaaS platforms lock you into their infrastructure and pricing. We deploy in your AWS account (BYOA) giving you full control, security, and cost transparency.
vs Build-It-Yourself: Building from scratch requires months of engineering effort and ongoing maintenance. We provide a proven framework with best practices built-in, ready to deploy in days.
vs Low-Code-Only Tools: Low-code platforms hit technical ceilings when you need advanced capabilities. Our framework supports both visual and code-based development with YAML-based agents for maximum flexibility.
No competitor delivers this combination: Complete framework + BYOA deployment + YAML-based CI/CD + enterprise-grade governance unified in one platform.