# 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 1. **Composability over monoliths** — Small, focused agents + orchestration > one giant agent 2. **Explicit state** — Serializable, inspectable, migratable state is non-negotiable 3. **Observability by default** — Every decision traceable; "why did it do that?" answerable 4. **Human-centered** — Autonomy levels configurable; meaningful human control preserved 5. **Cost-aware** — Token budgets, model routing, caching as first-class concerns 6. **Safety as architecture** — Guardrails, containment, verification built in, not bolted on 6. **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.*