AgenticEngineering — The Source of Truth for AI Agent Systems
A curated knowledge depot for building, deploying, and advancing autonomous AI agent systems. Covers foundations through frontier research.
Vault Structure
AgenticEngineering/
├── README.md # This file
├── Foundations/
│ ├── LLM-Fundamentals.md # Transformers, training, inference, scaling laws
│ ├── Agent-Definitions.md # What is an agent? Taxonomy, autonomy levels
│ ├── Core-Abstractions.md # Tools, memory, planning, reasoning, perception
│ └── History-Timeline.md # From Eliza to AutoGPT to present
├── Architectures/
│ ├── Single-Agent-Patterns.md # ReAct, ReWOO, Plan-and-Execute, Self-Ask
│ ├── Cognitive-Architectures.md # Soar, ACT-R, LIDA, modern neural-symbolic
│ ├── Control-Flow.md # Sequential, parallel, DAG, dynamic routing
│ └── Human-in-the-Loop.md # Approval, feedback, intervention patterns
├── Frameworks/
│ ├── LangGraph.md # Stateful graphs, checkpointing, streaming
│ ├── AutoGen.md # Multi-agent conversations, GroupChat
│ ├── CrewAI.md # Role-based, process-driven crews
│ ├── Semantic-Kernel.md # Planners, skills, Microsoft ecosystem
│ ├── LlamaIndex-Agents.md # Query engines, workflow agents
│ ├── PydanticAI.md # Type-safe, structured outputs
│ ├── AgentKit.md # LangChain's agent runtime
│ ├── OpenAI-Agents-SDK.md # Responses API, handoffs, guardrails
│ ├── Fringe-Frameworks.md # AgentOps, SuperAGI, MetaGPT, CAMEL, BabyAGI
│ └── Framework-Comparison.md # Decision matrix, migration paths
├── ContextEngineering/
│ ├── RAG-Fundamentals.md # Retrieval, indexing, chunking, embedding
│ ├── Advanced-RAG.md # HyDE, reranking, graph RAG, agentic RAG
│ ├── Memory-Systems.md # Short-term, long-term, episodic, semantic
│ ├── Context-Window-Management.md # Compression, summarization, prioritization
│ ├── Knowledge-Graphs.md # Entity extraction, Neo4j, GraphRAG
│ └── Multimodal-Context.md # Vision, audio, document understanding
├── ModelArchitectures/
│ ├── Mixture-of-Experts.md # MoE theory, routing, training, inference
│ ├── Model-Routing.md # Cascade, mixture-of-agents, dynamic selection
│ ├── Small-Language-Models.md # SLMs, distillation, quantization, edge
│ ├── Reasoning-Models.md # CoT, ToT, GoT, self-consistency, PRM
│ ├── Multimodal-Foundations.md # Vision-language, audio, unified models
│ └── Inference-Optimization.md # vLLM, SGLang, TensorRT-LLM, speculative decoding
├── StateManagement/
│ ├── Checkpointing.md # Serializable state, recovery, time-travel
│ ├── Session-Management.md # Conversation threads, multi-user, persistence
│ ├── Event-Sourcing.md # Event logs, replay, audit trails
│ ├── Distributed-State.md # Redis, etcd, CRDTs, consensus
│ └── Versioning-Migration.md # Schema evolution, backward compatibility
├── ToolUse/
│ ├── Function-Calling.md # OpenAI, Anthropic, Gemini schemas
│ ├── MCP-Protocol.md # Model Context Protocol, servers, clients
│ ├── Code-Execution.md # Sandboxes, Jupyter, E2B, Modal, Daytona
│ ├── Browser-Automation.md # Playwright, Puppeteer, Stagehand
│ ├── API-Integration.md # REST, GraphQL, OpenAPI, auth patterns
│ ├── Custom-Tools.md # Tool design, validation, documentation
│ └── Tool-Discovery.md # Registries, dynamic loading, composition
├── MultiAgent/
│ ├── Communication-Protocols.md # A2A, message passing, shared memory
│ ├── Coordination-Patterns.md # Supervisor, swarm, hierarchy, marketplace
│ ├── Negotiation-Consensus.md # Voting, auction, conflict resolution
│ ├── Delegation-Handoff.md # Task decomposition, subtask routing
│ ├── Emergent-Behavior.md # Self-organization, stigmergy, swarms
│ └── Safety-Coordination.md # Containment, monitoring, circuit breakers
├── Domains/
│ ├── Coding-Agents.md # Repo understanding, generation, refactoring
│ ├── Research-Agents.md # Literature review, hypothesis, experimentation
│ ├── Customer-Facing-Agents.md # Support, sales, onboarding, personalization
│ ├── Data-Analysis-Agents.md # SQL, BI, visualization, insight generation
│ ├── Creative-Agents.md # Writing, design, video, music generation
│ ├── Scientific-Agents.md # Lab automation, simulation, discovery
│ └── Infrastructure-Agents.md # DevOps, SRE, deployment, monitoring
├── Evaluation/
│ ├── Agent-Benchmarks.md # SWE-bench, GAIA, AgentBench, τ-bench
│ ├── Evaluation-Frameworks.md # LangSmith, AgentOps, Arize, Patronus
│ ├── Metrics.md # Success rate, latency, cost, quality, safety
│ ├── Offline-Eval.md # Datasets, golden sets, regression testing
│ ├── Online-Eval.md # A/B, canary, shadow, user feedback
│ └── Red-Teaming.md # Jailbreaks, prompt injection, adversarial
├── Production/
│ ├── Deployment-Architecture.md # Serverless, containers, edge, hybrid
│ ├── Scaling-Strategies.md # Horizontal, vertical, queue-based, async
│ ├── Observability.md # Tracing, logging, metrics, debugging
│ ├── Cost-Optimization.md # Caching, model selection, token budgets
│ ├── Reliability.md # Retries, fallbacks, circuit breakers, SLOs
│ ├── Security.md # Auth, authorization, data privacy, PII
│ └── Compliance.md # Audit logs, GDPR, SOC2, explainability
├── Frontiers/
│ ├── Self-Improving-Agents.md # RLHF, RLAIF, constitutional AI, evolution
│ ├── Lifelong-Learning.md # Continual learning, catastrophic forgetting
│ ├── Agent-Computer-Interface.md # ACL, desktop control, OS integration
│ ├── Multi-Modal-Agents.md # Vision, audio, video, robotics
│ ├── Agent-Societies.md # Civilization simulation, economics, governance
│ ├── Neurosymbolic-Integration.md # Logic + neural, program synthesis
│ ├── Formal-Verification.md # Proof-carrying agents, model checking
│ └── Research-Directions.md # Open problems, conjectures, roadmaps
Quick Navigation by Goal
| If you're... | Start Here |
|---|---|
| New to agents | Foundations/Agent-Definitions.md → Architectures/Single-Agent-Patterns.md |
| Choosing a framework | Frameworks/Framework-Comparison.md |
| Building RAG/memory | ContextEngineering/RAG-Fundamentals.md → ContextEngineering/Memory-Systems.md |
| Optimizing models | ModelArchitectures/Mixture-of-Experts.md → ModelArchitectures/Inference-Optimization.md |
| Managing state at scale | StateManagement/Checkpointing.md → StateManagement/Distributed-State.md |
| Building multi-agent | MultiAgent/Communication-Protocols.md → MultiAgent/Coordination-Patterns.md |
| Coding agent | Domains/Coding-Agents.md |
| Research agent | Domains/Research-Agents.md |
| Productionizing | Production/Deployment-Architecture.md → Production/Observability.md |
| Frontier research | Frontiers/Self-Improving-Agents.md → Frontiers/Research-Directions.md |
Cross-Cutting Principles
- Composability over monoliths — Small, focused agents + orchestration > one giant agent
- Explicit state — Serializable, inspectable, migratable state is non-negotiable
- Observability by default — Every decision traceable; "why did it do that?" answerable
- Human-centered — Autonomy levels configurable; meaningful human control preserved
- Cost-aware — Token budgets, model routing, caching as first-class concerns
- Safety as architecture — Guardrails, containment, verification built in, not bolted on
- Standards-based — MCP, A2A, OpenAPI, OpenTelemetry — avoid vendor lock-in
Key Resources to Track
| Category | Resources |
|---|---|
| Papers | ArXiv: cs.AI, cs.CL, cs.LG, cs.MA — daily scans |
| Benchmarks | SWE-bench, GAIA, AgentBench, τ-bench, WebShop, ALFWorld |
| Communities | LangChain Discord, AutoGen Discord, r/LLMDevs, AI Engineer Slack |
| Newsletters | The Batch, Import AI, TLDR AI, Latent Space, Interconnects |
| Conferences | NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, AgentConf |
Contributing
This vault is a living document. When you discover a valuable pattern, paper, or technique:
1. Find the relevant category folder
2. Create or update the appropriate .md file
3. Link related notes with Wikilinks
4. Cite sources (paper URLs, blog posts, docs)
5. Keep entries practical — prefer runnable patterns over theory
Curated from: ArXiv daily papers, top-tier conference proceedings (NeurIPS/ICML/ICLR/ACL), framework documentation, production post-mortems from leading AI labs, and practitioner communities.