Interactive Tools
Practical tools to help you think systematically, build better AI agents, and master prompt engineering.
Problem Solver
A structured thinking space to go from raw problem → root cause → ideas → architecture decisions → concrete build plan.
Problem Statement Builder
Define what you want to build in 5 steps — then generate a ready-to-use AI coding prompt.
AI Agent Skills Builder
Design custom AI agent skills based on best practices from Claude, Gemini, OpenAI, and GitHub Copilot.
Prompt Engineering
An interactive workspace to learn and test professional prompt engineering structures.
Enterprise Pattern
Design and visualize cognitive patterns for enterprise AI agents.
Trip Planner
Design a 6-phase, multi-agent AI pipeline for planning a comprehensive family trip.
The Complete Agentic AI Reference
Every aspect of designing, securing, and governing AI agents — balancing autonomous execution with human oversight, guardrails, and responsible control at every layer.
Core Architecture
Core patterns for agents that reason, act, and defer to humans when it matters
The Agent Loop
Think-Act-Observe cycle — how LLMs become engineers, with configurable checkpoints for human review and approval.
Agent Memory
Multi-tiered persistent context — working, episodic, semantic, and procedural memory stores.
Agent Skills
Portable, version-controlled knowledge packages for specialized agent capabilities.
Open Knowledge Format
Google's minimal, interoperable format for representing organizational knowledge in agents.
Orchestration & Operations
Scale and operate agents with human supervision, cost controls, and operational guardrails
Fleet Engineering
Orchestrate large agent deployments with lifecycle policies, human escalation paths, and SRE-grade observability.
Agentic Resource Discovery
Open federated spec for publishing, discovering, and verifying AI capabilities — DNS for agents.
AI Token Economy
Token pricing, cost optimization, caching strategies, and enterprise AI economics at scale.
Prompt Engineering
Interactive workspace for professional prompt structures — chain-of-thought, few-shot, and more.
Security & Governance
Defend, audit, and govern agents — keeping humans in control of critical decisions
Securing AI Agents
Google DeepMind's framework — AI Control Roadmap, GRAM, and three layers of agent security.
OWASP Agentic AI Top 10
The 10 critical risks for agentic systems — why human oversight and control boundaries are non-negotiable (2026).
AI Security
Threat taxonomy — data poisoning, adversarial attacks, supply chain, and AI-driven exploits.
Explainable AI (XAI)
Thinking traces and trajectory diagnostics that make agent decisions auditable and explainable to human reviewers.
Data Protection & Privacy
Masking, tokenization, PII redaction, and synthetic data with pre/post LLM hooks for agents.
Strategy & Domain Applications
Adopting agentic AI responsibly — from supervised pilots to measured autonomy at scale
AI Adoption Principles
Staged adoption framework from traditional ML to Agentic AI — with HITL guardrails at each transition across healthcare, airlines, and insurance.
Healthcare Lakehouse
5-tier medallion architecture for agentic AI in healthcare — with dynamic orchestration between autonomous execution and clinical human-in-the-loop.
Agent Skills Builder
Interactive tool to design custom AI agent skills following best practices from Claude, Gemini, and GPT.
Agentic AI Catalog
Full catalog of agentic AI patterns, frameworks, and reference architectures.
Explore Software Engineering Foundations
The Blueprint
OOP and Design Patterns - the foundation of clean, maintainable software architecture.
- Explore Pattern Categories
- Explore OOP Concepts
Explore Pattern Categories
The Engine
Algorithms, Data Structures, and Big-O complexity - the performance fundamentals.
- Algorithms
- Data Structures
- Big-O Complexity
Algorithms
Data Structures
The Guardrails
SOLID, DRY, YAGNI, KISS, Code Smells, and OWASP Security - principles that keep your code clean, maintainable, and secure.
- SOLID Principles
- DRY, YAGNI & KISS
- Code Smells
- AI Security Threat Landscape
- OWASP Security Top 10
- OWASP LLM Top 10
- OWASP Agentic AI Top 10
- OWASP AIVSS
- OWASP Citizen Development
- Guardrails ACS
SOLID Principles
Code Smells
AI Security Threat Landscape
OWASP Security Top 10
OWASP LLM Top 10
OWASP Agentic AI Top 10
OWASP AIVSS
OWASP Citizen Development
Trade-offs
In engineering and AI, there are no solutions—only trade-offs. Navigate the fundamental constraints across distributed systems, ML, LLMs, cloud infrastructure, and frameworks with best practices for optimal decisions.
- CAP, PACELC, ACID vs BASE
- Bias-Variance, Precision-Recall
- Alignment Tax, HHH Trilemma
- Spot vs On-Demand, Serverless
- Training vs Inference Optimization
- JAX vs PyTorch, Framework Choices
Distributed Systems
Data integrity vs system velocity: CAP theorem, PACELC, ACID vs BASE, consistency models, and replication strategies
Machine Learning
Predictive power vs operational realities: bias-variance tradeoff, precision-recall, regularization, and model complexity
LLMs
Capability vs safety and control: alignment tax, HHH trilemma, context window limits, and RAG vs parametric memory
Cloud Infrastructure
Financial efficiency vs reliability: spot vs on-demand vs reserved, serverless vs containers, multi-cloud strategies
AI Infrastructure
High throughput vs low latency: training vs inference optimization, parallelism strategies, GPU vs TPU vs CPU, quantization
Frameworks
Developer velocity vs hardware abstraction: JAX vs PyTorch, static vs dynamic graphs, and framework ecosystem decisions
Architectural Primitives for Non-Deterministic Software
Software engineering is shifting from a deterministic paradigm to a probabilistic one — two foundational primitives constrain how that non-determinism gets executed
Treating an LLM as a magical black box produces brittle, unscalable code. Instead of deploying a single, massive prompt to handle all logic, high-performance systems decouple execution into a routing layer that picks the cheapest correct path, and a self-correcting validation layer that guarantees type safety before anything reaches downstream systems.
Semantic Routing
Inbound payloads are evaluated by low-latency, specialized routers using text embeddings or compact, fine-tuned models. The router determines intent and structurally directs the payload to the most cost-effective execution path, isolating simple extraction tasks from deep reasoning workflows and dropping system latency and compute costs.
Validator-Corrector Loops
Rather than wrapping API responses in generic error handling, output parsing is structured as a closed-loop state machine. A deterministic code layer — a Pydantic schema validator, for example — catches the exact exception, formats it into an atomic correction log, and feeds it back to the model as a targeted system prompt before data updates any downstream system.
Agentic AI Pattern
Perceive
Gather information from the environment through sensors, tools, and inputs
Plan
Formulate a strategy and break down complex tasks into actionable steps
Act
Execute planned actions using available tools and capabilities
Observe
Monitor the results of actions and assess their impact on the environment
Iterate
Refine approach based on observations and continue the cycle until goals are met
Perceive
Gather information from the environment through sensors, tools, and inputs
Plan
Formulate a strategy and break down complex tasks into actionable steps
Act
Execute planned actions using available tools and capabilities
Observe
Monitor the results of actions and assess their impact on the environment
Iterate
Refine approach based on observations and continue the cycle until goals are met
Automated B2B Invoice Reconciliation
A price mismatch doesn't have to trigger a generic system failure — an agentic loop can resolve it, with a hard checkpoint before anything touches the ledger.
- PerceiveIngests the mismatched invoice payload.
- PlanDecides it must look up the original Purchase Order and cross-reference vendor contract histories.
- ActExecutes internal database lookups via highly restricted API tools.
- ObserveIdentifies that a temporary vendor discount was omitted from the invoice text.
- HITL GatewayBefore moving to the final iteration to update the ledger, the state machine hits a hard checkpoint. It serializes the agent's complete execution trace into an immutable schema, presents it to a financial auditor for authorization, and freezes execution until explicit human approval is received.
Knowledge Format Specification
Open Knowledge Format (OKF) — A minimal, interoperable format for knowledge representation in agentic AI systems
Knowledge Bundle
A self-contained directory tree of knowledge documents — the unit of distribution. Organizes concepts hierarchically. Preferred distribution is a Git repository (history, attribution, diffs, PR reviews for free).
Concept
A single unit of knowledge — one markdown file. Can represent tables, APIs, metrics, runbooks, or any knowledge unit. File path within the bundle (minus .md) is the Concept ID.
Frontmatter
YAML metadata block at the top of each file. Only required field is `type` — a free-form string. Consumers must tolerate unknown types gracefully. Additional custom fields are always valid.
Cross-Linking
Standard markdown links between concepts build the knowledge graph. Prefer absolute bundle-relative paths (/path/to/concept.md) for stability. Broken links are valid — link to a not-yet-written concept is not malformed.
Index & Log Files
Reserved filenames with defined meaning. index.md provides progressive disclosure of available concepts. log.md tracks chronological change history (newest first, ISO 8601 dates). Both can appear at any directory level.
Agent Knowledge Packaging
Agent Skills — Portable, version-controlled packages of specialized knowledge for AI agents
Skill Structure
A folder with SKILL.md (required), plus optional scripts/, references/, and assets/ directories. The directory name must exactly match the name field in frontmatter.
Progressive Disclosure
Three-level loading system: Discovery (~100 tokens at startup), Activation (<5,000 tokens on-demand), Execution (on-demand). Install 50 skills for only ~5,000 tokens startup cost.
SKILL.md
The heart of every skill. YAML frontmatter contains name and description (the semantic trigger). Body contains instructions, gotchas, checklists, and validation loops. Keep under 5,000 tokens.
Scripts Directory
Executable code for deterministic operations. The agent runs tested code instead of regenerating it each session. Scripts accept CLI args, emit structured JSON, never consume context until called.
References Directory
Deep documentation loaded on-demand. Each file focuses on one topic. Tell the agent exactly when to load each file in SKILL.md. Heavy reference docs never touch context unless called.
Ecosystem
Open standard at agentskills.io (Dec 2025) adopted by Claude, Cursor, GitHub Copilot, Gemini CLI, and 20+ platforms. 1,525+ community skills via Antigravity library. Official repos from Notion, Vercel, Supabase, Microsoft.
Agent Persistent Context
Agent Memory — Multi-tiered persistent context retention and state management for autonomous AI agents
Working Memory
Immediate in-context workspace (the active context window). Ephemeral, fast, and token-bounded. Holds the current conversation thread, active tool outputs, and short-term reasoning traces.
Episodic Memory
A timestamped record of specific experiences, user interactions, and decisions. Preserves instance-specific contexts (the who, what, when, and why) to prevent loss of task continuity across session boundaries.
Semantic Memory
Abstracted, generalized facts, domain concepts, organizational rules, and user preferences. Evolved and updated over time with active conflict resolution to maintain factual consistency.
Procedural Memory
Standard operating procedures, routing workflows, coding standards, and skill sets (such as SKILL.md libraries). Allows the agent to execute complex rules automatically without reasoning from scratch.
Vector & Graph Stores
The physical execution layer. Vector stores handle fast fuzzy semantic similarity, while temporal graph stores manage entity relationships and multi-hop reasoning over time.
Loop Engineering
Design autonomous systems that prompt AI agents — replacing manual prompting with recursive, goal-driven architectures
Automations
Heartbeat loops, cron schedules, and goal-driven execution that surface work automatically
Worktrees
Git worktrees for parallel agent isolation, preventing file collisions in concurrent workflows
Skills
Codified project knowledge that compounds understanding across sessions
Connectors
MCP-based plugins for real tool integration — issue trackers, databases, APIs, and Slack
Subagents
Separate maker from checker — specialized agents for exploration, implementation, and verification
State/Memory
Persistent memory that remembers what was tried and what remains across sessions
AI Token Economy
From first principles to full production — every layer of cost, craft, and strategy in the age of agentic AI
Token Economics
Understand the atomic unit of AI work and how pricing models impact your bottom line
Cost Optimization
Prompt engineering, context management, and caching strategies to reduce token usage by 30-50%
Enterprise Strategy
Vendor selection, TCO analysis, and building internal AI expertise for scalable adoption
Risk Management
Cost monitoring, quality controls, and governance frameworks for production AI systems
Optimizing Token Usage Practices
Three-layer reference for token optimization across AGENTS.md, CLAUDE.md and friends across five coding agent tools
Layer 1: Agent/Platform Mechanics
System-level configuration: prompt caching, MCP setup, toolset scope, and model routing
Layer 2: Agent Operation
Session discipline: subagent delegation, compaction strategy, and usage monitoring
Layer 3: Codebase Architecture
Code organization: SRP, coupling, file size, and hierarchical structure for minimal context
Tool-Specific Guides
Implementation patterns for Claude Code, Codex CLI, Antigravity CLI, Devin CLI, and Cursor
Data Protection & Privacy
Masking, Anonymization, Tokenization, Pseudonymization, PII Redaction, and Synthetic Data Generation — with pre/post LLM hooks and agent-native implementation patterns
Masking ↩️ Reversible
Replace sensitive values with structurally consistent placeholders while preserving format for testing and analytics
Anonymization 🔒 Irreversible
Irreversible removal or transformation of personal data so individuals cannot be re-identified, even with additional sources
Tokenization ↩️ Reversible
Replace sensitive values with random tokens stored in a secure vault, enabling controlled reversibility and strong compliance
Pseudonymization ↩️ Reversible
Replace identifiers with artificial aliases while maintaining separate keys for re-identification under strict governance
PII Redaction 🔒 Irreversible
Permanent, irreversible removal of sensitive information from documents, ideal for legal publishing and erasure requests
Synthetic Data
Generate statistically faithful test data with zero real PII exposure for prompt engineering, QA, and model training
Agentic Resource Discovery
ARD — An open, federated specification for publishing, discovering, and cryptographically verifying AI capabilities across organizational boundaries
Catalogs
Static JSON manifest files (ai-catalog.json) hosted at your organization's domain at a well-known path. Domain ownership becomes the cryptographic foundation for identity and trust.
Registries
Searchable, dynamic services that crawl published catalogs, index their entries, and expose a REST search API. Registries are the 'search engines' of the agentic web.
URN Identifiers
Globally unique identifiers following the format urn:ai:<publisher>:<namespace>:<agent-name>. Domain-anchored format provides nomenclature stability and cross-network uniqueness.
Trust Manifest
Cryptographic identity and compliance metadata. Separates discovery identifier (URN) from runtime authentication identity (SPIFFE ID, DID, or HTTPS FQDN).
REST API
Standard HTTP REST interface for search. POST /search for semantic search, POST /explore for faceted aggregation. Universal baseline enables federation between registries.
Federation
Connect individual registries into a global network. Client controls federation mode: auto (registry merges results), referrals (client follows), or none (single registry only).
Fleet Engineering
The emerging discipline of managing large-scale deployments of autonomous AI agents as a unified operational system — from orchestration to observability
Orchestration Layer
The control plane responsible for task decomposition, agent selection, dependency management, and state tracking. Frameworks include LangGraph, Microsoft AutoGen, and Anthropic's multi-agent patterns.
Deployment & Versioning
Prompt versioning tracked in version control with testing before promotion. Model versioning for different agent types. Blue/green or canary deployments for safe rollouts.
Observability & Monitoring
Purpose-built tooling for tracing full reasoning chains, tool calls, and agent handoffs. Span and step logging, latency/cost tracking, and automated evaluation metrics at runtime.
Resource & Rate Limit Management
API rate limit management with backoff and retry logic. Compute scheduling for parallel execution. Token budget allocation and cost attribution per agent/team/task.
Agent Lifecycle Management
Spawning, execution, checkpointing, termination, and failure recovery. Long-running agents save intermediate states for resumption after interruption.
Human-in-the-Loop Checkpoints
Explicit checkpoints for human review before irreversible actions, low-confidence decisions, scope changes, and scheduled audits for long-running agents.
Security & Isolation
Prompt injection defense with input sanitization. Principle of least privilege for tool access. Agent sandboxing in isolated containers/VMs. Comprehensive audit trails.
Evaluation & Quality Assurance
Unit evaluation with standardized benchmarks. Integration evaluation for multi-agent pipelines. Online evaluation with automated scoring. Regression testing before deployments.
Cloud Native Ecosystem
Leveraging Kubernetes, containers, service meshes, and cloud-native observability stacks as the infrastructure layer for AI agent fleets. Includes orchestration, scaling, security, and GitOps patterns.
Explainable AI (xAI)
Building institutional-grade xAI frameworks for advanced LLM agents in regulated environments — from thinking traces to memory observability
Thinking Traces
Capture Chain-of-Thought reasoning paths for safety monitoring and goal drift detection in clinical decision support
Memory Observability
Audit multi-tier memory operations to track context provenance and prevent error propagation across clinical workflows
Trajectory Diagnostics
Move beyond feature attribution to audit complete execution graphs for regulatory compliance and clinical validation
Causal Validation
Automated counterfactual evaluation to verify that captured reasoning is causally linked to clinical outcomes
AI Adoption Guiding Principles
A strategic framework for AI adoption from traditional machine learning to agentic AI in healthcare, airlines, and insurance
Traditional ML
Structured prediction & inference — answering "What is likely to happen?"
Generative AI
Language understanding & generation — answering "What should this look like?"
Agentic AI
Autonomous multi-step orchestration — answering "What should I do next?"
Healthcare
Clinical risk scoring to autonomous care coordination and prior authorisation workflows
Airlines
Revenue management to dynamic mass disruption re-accommodation and autonomous turnaround orchestration
Insurance
Actuarial pricing to autonomous subrogation recovery and straight-through claims processing
Scaling Agentic AI in Healthcare Lakehouse
Cloud-agnostic healthcare architecture for scaling autonomous AI agents across enterprise data platforms — from Bronze to Platinum layers
5-Tier Medallion
Bronze to Platinum architecture for raw ingest, operational state, analytics, semantic spine, and agent control plane
Privacy Hooks
Pre-LLM and Post-LLM hooks for PII redaction, tokenization, pseudonymization, masking, and synthetic data generation
HITL & Autonomous
Dynamic orchestration between fully autonomous execution and human-in-the-loop clinical guardrails
Cloud-Agnostic
Kubernetes-native deployment across AWS, Azure, GCP, OpenShift, and on-premises without vendor lock-in
Ontological Engineering in AI
Formal, machine-checkable models of concepts, relationships, and constraints for grounding agentic AI systems and LLMs
Formal Ontologies
Building, validating, and maintaining reusable, verifiable artifacts using RDF, OWL, and SHACL schemas.
Neuro-Symbolic AI
Combining statistical language fluency with symbolic precision for accountability and provenance.
Knowledge Graphs
Grounding LLMs on verified relational context via GraphRAG, MemGraphRAG, and semantic layers.
LLM-Augmented
Bootstrapping term extraction and relation mapping using multi-agent specialization pipelines.
Securing AI Agents
Google DeepMind's comprehensive framework for defending systems against potentially misaligned AI agents
AI Control Roadmap
A tiered, measurable framework for defending systems against misaligned agents
Gram Framework
Empirical evaluation framework for measuring sabotage propensity in models
Three Layers of Security
A framework to secure the entire agent ecosystem from deployment to market governance
Large Quantitative Models (LQMs)
Neural networks trained on lab data and scientific equations — computing real-world numerical outcomes instead of generating language. The emerging layer beneath reasoning LLMs in enterprise AI stacks.
What LQMs Are
Physics-grounded models for precise numerical prediction
Physics-Grounded Data
Trained on quantum chemistry, molecular dynamics, and microkinetics — not internet text
Numerical Precision
Predicts adsorption energy, binding affinity, and toxicity risk with scientific accuracy
LLM Tool Integration
Called as tools via MCP — reasoning LLMs (Claude, Gemini) orchestrate at the interface layer
Model Catalog
SandboxAQ's shipping LQMs — biopharma, materials & catalysis
AQCat · Catalysis
Adsorption-energy screening — live via Claude MCP; Google Cloud Marketplace Q3 2026
AQPotency · Drug Discovery
High-throughput drug-binder identification — Google Cloud Marketplace Q3 2026
AQCell · Cell Biology
Simulates cell response to drug candidates; flags liver toxicity and pathway activation
The Frontier Lab Race
Three labs, three strategies for AI in physical science
Anthropic · Claude Science
LQMs via MCP + 60+ scientific databases + sub-agent orchestration (June 30, 2026)
Google · Gemini for Science
Co-Scientist, AlphaFold, AlphaGenome + SandboxAQ LQMs via Cloud Marketplace
OpenAI · GPT-Rosalind
Fine-tuned LLM for life sciences reasoning — genomics, drug discovery, LifeSciBench
Enterprise Architecture Pattern
How LQMs slot into agentic AI platform stacks
Reasoning Interface Layer
LLM (Claude, Gemini, GPT) handles natural-language orchestration and decision-making
Domain-Grounded Compute Tier
LQMs provide physics- or domain-constrained numerical ground-truth via MCP tool calls
Open Challenges
Interpretability in regulated domains and data drift from abrupt physical-system shifts
Tabular Foundation Models (TFMs)
Zero-shot foundation models for spreadsheets and database tables — no per-dataset training, no hyperparameter tuning, no feature engineering. The next frontier of enterprise AI infrastructure.
What TFMs Are
Zero-shot prediction on structured tabular data
In-Context Learning
Models see the entire table as a single prompt and predict in one forward pass
Alternating Attention
Row/column cross-attention respects tabular structure — orderless by design
SQL-Native Distribution
Embedded directly in databases (BigQuery, Redshift, SAP HANA) as AI.PREDICT verbs
Model Catalog
Production TFMs from hyperscalers and enterprise vendors
Google · TabFM
Hybrid TabPFN + TabICL architecture; BigQuery AI.PREDICT integration (June 2026)
Amazon · Mitra
Bundled in AutoGluon; benchmarked on TabArena, TabRepo, TabZilla
SAP · RPT-1 / RPT-1.5
Trained on real enterprise data with semantic embeddings; €1B+ Prior Labs acquisition
Competitive Landscape
Hyperscalers vs. frontier labs: two different paradigms
Database-Embedded TFMs
Google, AWS, Microsoft, SAP — own the data platform, ship native SQL predictors
Agentic Code Execution
Anthropic & OpenAI: LLMs write/run pandas/scikit-learn code instead of specialized models
Research Lineage
TabPFN (2022) → TabICL (2025) → TabFM (2026); SAP's ConTextTab adds semantic knowledge
Enterprise Architecture Impact
How TFMs reshape ML infrastructure and data readiness
MLOps Compression
Multi-day hyperparameter sweeps → single API call; democratizes predictive modeling
Data Readiness Bottleneck
Shifts from model quality to data-platform quality: schema governance, lineage, semantic layers
Convergence Pattern
LLMs orchestrate TFMs as tools via MCP — natural division of labor between reasoning and prediction
Software Engineering Playbooks
Practical, End-to-End implementation guides for building Production-ready Software. Each playbook includes working code, architecture diagrams, and step-by-step instructions.
Research Agent with Gateway
Build a production-ready AI research agent using Agent Gateway for unified traffic management, authentication, and observability across LLM providers.
RAG Pipeline with Vector Database
Implement a complete Retrieval-Augmented Generation pipeline with vector embeddings, semantic search, and context injection for accurate AI responses.
Multi-Agent Orchestration
Create a coordinated multi-agent system with specialized agents, task distribution, and result synthesis for complex problem-solving.
Future References
Explore these resources for deeper learning on AI agent development, spec-driven development, and prompt engineering tools.
Spec-Driven Development
Comprehensive guide on Spec-Driven Development practices and methodologies.
Awesome Copilot
Curated list of GitHub Copilot resources, extensions, and best practices.
Promptfoo
Tool for testing, evaluating, and improving LLM prompts and applications.
Prompts.chat
Collection of prompt engineering resources and templates.
Agent Skills
Agent skills resources and documentation for building AI agent skills.
Awesome Skills
Curated list of awesome skill repositories and collections.
Agent Skills Topic
GitHub topic for discovering agent-related skills and repositories.
AI Agent Topic
Trendshift topic for discovering AI agents.
AI Skills Topic
Trendshift topic for discovering AI skills.
Agent Governance Toolkit
Agent governance toolkit.
Pattern Sources
Our patterns are curated from industry-leading sources with proper attribution and licensing compliance.
Refactoring.Guru
Classic GoF design patterns, code smells catalog, and refactoring techniques (https://refactoring.guru).
Enterprise Integration Patterns
65 messaging patterns for integrating enterprise applications by Gregor Hohpe and Bobby Woolf (CC BY 4.0).
Microservices.io
Comprehensive patterns for microservice architectures by Chris Richardson.
Agent Catalog Patterns
Patterns for agentic systems from agentpatternscatalog.org (CC BY 4.0).
OWASP Foundation
Security patterns from OWASP Top 10 for Web Applications, LLM Applications, and Agentic Applications (CC BY-SA 4.0).
Industry Research
ML/AI patterns from Microsoft, Google, Anthropic, and academic research.
AI Agent Patterns
Spec-driven development patterns from Claude, Gemini, OpenAI, and GitHub Copilot on github/spec-kit and OpenSpec.
Data Engineering Leaders
Data platform patterns from Martin Fowler (Data Mesh), Kimball Group (Dimensional Modeling), and cloud providers.
MLOps Best Practices
Data science patterns from MLflow, Great Expectations, and MLOps practitioners.
Streaming & Analytics
Real-time patterns from Confluent/Kafka, Apache projects, and serverless analytics platforms.
Academic Papers
Rigorous ML patterns from peer-reviewed research including data leakage prevention and active learning.
5-Day AI Agents Course
Intensive Vibe Coding Course With Google by Brenda Flynn et al. (2026) on Kaggle.
The Agent Loop
Foundational Agent Definition (Perceive + Act):
Russell, S. J., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall. (Current edition: 4th Ed., Pearson, 2020)
Modern Iterative LLM Agent Loop:
Yao, S., Zhao, J., Yu, D., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629.
Historical Context:
Incorporating AIMA's perceive/act model, Classical robotics' Sense-Plan-Act loop (Brooks, 1986), and ReAct's Thought→Action→Observation cycle.
Glossary A-Z Index
Quick navigation to agentic AI terminology and concepts