Источники¶

Ниже собраны основные первоисточники, на которые опирается текущая версия книги. Дата обращения: 22 апреля 2026 года.

Нормативные рамки и контуры управления¶

• OWASP, LLM Prompt Injection Prevention Cheat Sheet
• NIST, AI RMF 1.0
• NIST, AI RMF: Generative AI Profile
• NIST, SP 800-53 Rev. 5: Security and Privacy Controls for Information Systems and Organizations
• NIST, SP 800-218A: Secure Software Development Practices for Generative AI and Dual-Use Foundation Models
• NIST, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
• CISA, Artificial Intelligence

Архитектура агентных систем и платформенные паттерны¶

• Дмитрий Викулин, «Архитектура надежных AI-агентов»
• Anthropic, Building Effective AI Agents
• Anthropic, Harness design for long-running application development
• OpenAI, A practical guide to building agents (PDF)
• OpenAI, Agents SDK
• OpenAI Agents SDK, Sandbox Agents, Sandbox Concepts, Sandbox clients и Agent memory
• OpenAI, Agent Builder
• LangGraph, Overview
• LangGraph, Durable execution
• LangGraph, Persistence
• LangGraph, Memory overview
• LangChain, Multi-agent
• Google Cloud, Achieve agentic productivity with Vertex AI Agent Builder
• Google Cloud, More ways to build, scale, and govern AI agents with Vertex AI Agent Builder
• Google Cloud, Vertex AI Agent Builder overview
• Google Cloud Architecture Center, Multi-agent AI system in Google Cloud
• Microsoft Azure Architecture Center, AI Agent Orchestration Patterns
• Cloudflare, Build Agents on Cloudflare
• Cloudflare Agents SDK, Store and sync state и Schedule tasks
• Cloudflare Agents SDK, Human in the Loop и WebSockets
• Cloudflare, Build and deploy Remote Model Context Protocol (MCP) servers to Cloudflare

Наблюдаемость, evals и verifier design¶

• OpenAI, Agent evals
• OpenAI, Trace grading
• OpenAI, Background mode
• OpenAI, Using tools
• OpenAI, Structured model outputs
• Microsoft Learn, Observability for Generative AI and agentic AI systems
• Google Cloud, Observability and monitoring
• AWS, Introducing stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime
• arXiv, The Art of Building Verifiers for Computer Use Agents
• GitHub, microsoft/fara

HCI, HITL и человеческий надзор¶

• Microsoft Research, Guidelines for Human-AI Interaction
• LangChain Deep Agents, Human-in-the-loop
• LangGraph, Interrupts
• OpenReview, The Illusion of Consensus in Human-Centered Interactive AI
• Microsoft Learn, Agentic AI adoption maturity model

Governance, security и operational assurance¶

• Google Cloud, How Google secures AI Agents
• Google Cloud, Recommended AI Controls framework
• Google Cloud, Introducing Agent Sandbox
• Google Research, Security Assurance in the Age of Generative AI
• Google Research, Securing the AI Software Supply Chain
• Google Research, An Introduction to Google’s Approach for Secure AI Agents
• Google Research, Identifying and Mitigating the Security Risks of Generative AI
• Anthropic, Claude Code Security
• Anthropic, Agentic Misalignment
• Anthropic, Strengthening Red Teams
• Anthropic, Introducing Bloom
• Anthropic, Findings from a Pilot Anthropic-OpenAI Alignment Evaluation Exercise
• MLCommons, AILuminate v1.0 Release
• Microsoft Learn, Secure autonomous agentic AI systems
• Microsoft Learn, Reduce autonomous agentic AI risk
• Microsoft Learn, Complete production infrastructure inventory
• Microsoft Learn, Agent Registry convergence with Microsoft Agent 365

Инциденты и кейсы¶

• American Bar Association, BC Tribunal Confirms Companies Remain Liable for Information Provided by AI Chatbot

Исследовательский фронтир: память, observability и multi-agent reliability¶

• OpenReview, EVOLVE-MEM: A Self-Adaptive Hierarchical Memory Architecture for Next-Generation Agentic AI Systems
• OpenReview, MemGen: Weaving Generative Latent Memory for Self-Evolving Agents
• OpenReview, AgentTrace: A Structured Logging Framework for Agent System Observability
• OpenReview, AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems
• OpenReview, Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy
• OpenReview, AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
• OpenReview, Aegis: Automated Error Generation and Attribution for Multi-Agent Systems
• OpenReview, PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
• OpenReview, Why Do Multiagent Systems Fail?

Публикация, сборка и платформенный слой книги¶

• MkDocs, Official documentation
• Material for MkDocs, Official documentation
• uv, Working on projects
• ty, Official documentation
• Starlight, Official documentation

Rust и инфраструктурный слой агентных рантаймов¶

• AWS, AWS SDK for Rust is generally available
• AWS Docs, Code examples for Amazon Bedrock Runtime using AWS SDK for Rust
• docs.rs, aws-sdk-bedrockagentruntime
• Microsoft Learn, Azure SDK for Rust
• Rig, Official documentation
• docs.rs, rig-core
• GitHub, 0xPlaygrounds/rig

Как использовать этот список¶

Если развивать книгу дальше, удобнее идти в таком порядке:

1. Нормативный каркас риска и контроля: NIST, OWASP, CISA.
2. Архитектурные паттерны и runtime-дисциплина: Anthropic, OpenAI, LangGraph, Google Cloud, Microsoft.
3. Наблюдаемость, evals и verifier layer: OpenAI, Microsoft, arXiv, GitHub.
4. HCI, HITL и кейсы: Microsoft Research, OpenReview, ABA.
5. Исследовательский фронтир: memory, consistency, observability, multi-agent failure modes.

Для чтения самой книги полезно держать еще одну развилку:

• Устойчивое ядро: нормативные рамки, архитектура, policy, execution, наблюдаемость.
• Быстро меняющийся слой: инструменты evals, дизайн verifier'ов, inventory governance, frontier research и свежие кейсы.