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Agentic AI Knowledge Base

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1. Concepts
1. Concepts
2. Agent Harness
2. Agent Harness
3. Architecture and Design Patterns
3. Architecture and Design Patterns
- 3.1 Agentic Architecture Components Selection
- 3.2 Agentic Design Pattern Selection
  3.2 Agentic Design Pattern Selection
- 3.3 Multi-agent System
- 3.4 12-Factor Agents
- 3.5 Gartner LLM-based AI Design Patterns
- 3.6 Miscellaneous
4. Agent Development Frameworks
4. Agent Development Frameworks
4a. AI Coding Agents
4a. AI Coding Agents
- 4a.1 AI Coding Agents Overview
- 4a.2 Claude Code
5. Agent Technology Stack
5. Agent Technology Stack
- 5.1 Agent Tech Stack References
- 5.1.1 Thoughtworks Tech Radar — Agentic AI (Vol. 34, Apr 2026)
- 5.1.2 Semantic Data Layer Technology Radar
- 5.2 Agentic AI Platforms
  5.2 Agentic AI Platforms
- 5.3 Workflow Engine/Frameworks
  5.3 Workflow Engine/Frameworks
- 5.4 Popular AI Agents
- 5.5 Hermes Agent (Nous Research)
- 5.6 OpenClaw
- 5.7 OpenHuman (TinyHumans AI)
- 5.8 LifeOS (Daniel Miessler)
- 5.9 Discovery Loop
6. Agentic AI Industry Standards
6. Agentic AI Industry Standards
7. Agentic AI Reference Architecture
7. Agentic AI Reference Architecture
- 7.1 Reference Architecture Overview
- 7.2 AI Engineering Architecture (Huyen)
- 7.3 AI Assistant Reference Architecture
- 7.4 AI Assistant
- 7.4 AI Automation
  7.4 AI Automation
  - 7.4.1 AI Automation Overview
  - 7.4.2 LangManus – AI Automation Framework
- 7.5 Self-learning Agents
  7.5 Self-learning Agents
  - 7.5.1 Self-Learning Agents Overview
  - 7.5.2 Agent0 Series - Self-Evolving Agents from Zero Data
- 7.6 RAG Reference Architecture
- 7.7 Specialized Blueprints
- 7.8 RAG Implementation
- 7.9 Search as Code (Perplexity)
8. Context Engineering
8. Context Engineering
- 8.1 Key Challenges in Context Management
- 8.2 Common Strategies for Context Management
- 8.3 Context Graph
- 8.4 Prompt Engineering
  8.4 Prompt Engineering
- 8.5 Context Implementation References
  8.5 Context Implementation References
- 8.6 Efficiency Frontier (Cost-Performance)
9. Agent Memory Management
9. Agent Memory Management
10. Agentic AI Evaluation
10. Agentic AI Evaluation
11. Agentic AI Security
11. Agentic AI Security
- 11.1 Security Standards
  11.1 Security Standards
  - 11.1.1 NIST AI RMF (Risk Management Framework)
- 11.2 Google Perspective
  11.2 Google Perspective
  - 11.2.1 SAIF Framework
- 11.3 AWS Perspective
- 11.4 Microsoft Perspective
  11.4 Microsoft Perspective
  - 11.4.1 Agent Governance Toolkit
- 11.5 Agent Sandboxing
- 11.6 Anthropic Perspective
  11.6 Anthropic Perspective
  - 11.6.1 Sandbox Runtime (srt)
- 11.7 Skill Security Scanners
- 11.8 Agentic AI Red Teaming Guide (CSA)
- 11.9 Learn Prompt Hacking
12. Agent Observability
12. Agent Observability
13. Agentic AI Operations (AgentOps)
13. Agentic AI Operations (AgentOps)
- 13.1 AgentOps Overview
- 13.2 Google Cloud Perspective
  13.2 Google Cloud Perspective
  - 13.2.1 GenOps – Evolution of MLOps for GenAI
- 13.3 Kubernetes-Native Agent Orchestration
  13.3 Kubernetes-Native Agent Orchestration
14. Agentic AI Maturity Models
14. Agentic AI Maturity Models
- 14.1 Gartner's Perspective
- 14.2 AWS's Perspective
  14.2 AWS's Perspective
  - 14.2.1 Maturity Model for Generative AI
  - 14.2.2 AWS Maturity Model
- 14.3 Google's Perspective
- 14.4 IDC's Perspective
- 14.5 Maturity Models Overview
- 14.6 Agentic Engineering Levels
- 14.7 Arsanjani GenAI Maturity Model
15. Agents Marketplace
15. Agents Marketplace
16. Production Best Practices & Guidelines
16. Production Best Practices & Guidelines
17. AI Governance
17. AI Governance
18. Vendor Deep Dives
18. Vendor Deep Dives
- 18.1 Hyperscalers
  18.1 Hyperscalers
- 18.2 AI Labs
  18.2 AI Labs
  - 18.2.1 Anthropic
  - 18.2.2 OpenAI
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