Clawification — the shift to agents with bash access and markdown skill files, replacing tool definitions and MCP. Covers the skill primitive, platform implementations (OpenClaw, NemoClaw, NanoClaw, ZeroClaw and more), channel integrations (WhatsApp, Telegram, enterprise), and OpenRouter Spawn deployment.

Design patterns for building AI agents including reflection, planning, tool use, multi-agent systems, and autonomous workflows.

Core components and building blocks of AI agents including memory systems, tool interfaces, reasoning engines, and execution frameworks.

Taxonomy of agentic loop patterns (refinement, research, verification, reflection, exploration, nested), loop anatomy, loop control mechanisms, real implementations including autoresearch and Claude Code /loop, and loop-specific failure modes.

State machine executor that takes a declarative graph spec and runs it to completion. Five edge condition types, parallel fan-out/fan-in, crash recovery, continuous conversation threading, and two-tier retry with the judge system.

Memory systems for AI agents including short-term, long-term, semantic, and episodic memory architectures.

Strategies for managing conversation history within token limits, from simple sliding windows to semantic chunking and retrieval-augmented context.

V2V pipeline architecture, modality taxonomy (V2V, TTS, STT, hybrid), major platforms (ElevenLabs, Vapi, Retell, Bland, OpenAI Realtime, LiveKit), tool calling patterns in voice, latency constraints, and design principles.

Voice-to-voice agent risk catalogue covering cloning attacks, liveness injection, cross-platform deepfakes, and transcript poisoning — with gap analysis and third-party security tooling from Pindrop, Reality Defender, ID R&D, and Nuance.

ElevenLabs voice agent testing covering scenario evaluation with LLM judges, tool call exact-match validation, full and partial conversation simulations, CI/CD pipeline integration, test generation from real conversations, and the full API & SDK surface.

AI safety techniques including confessions for self-reporting misbehavior, scheming detection, deliberative alignment, chain-of-thought monitoring, and building robust safety stacks.

Zero Trust principles applied to AI agents, treating the model as an untrusted actor inside the perimeter with per-action gating, circuit breakers, and policy enforcement.

Guardrails for AI safety including content moderation, PII protection, prompt injection defense, hallucination detection, and implementation patterns.

Block-level monitoring architecture with pre-hooks, stream safeguards, and output guardrails forming a three-layer safety system for runtime agent intervention.

Catalogue of 15 failure modes specific to AI agents — from context collapse and goal drift to coordination deadlocks and hallucinated affordances.

The first comprehensive standard for AI agent security, safety, and trustworthiness. Six domains, independent third-party certification, and mappings to ISO 42001, EU AI Act, NIST AI RMF, and OWASP.

OWASP LLM Top 10, prompt injection defenses, model security, adversarial attacks, and AI-specific security measures.

Anthropic's interpretability research traced Claude's internal computations across tasks — finding that it thinks in language-agnostic concepts, plans ahead in poetry, uses computation strategies it cannot describe, and sometimes constructs reasoning post-hoc. With implications for CoT trust, hallucination causes, and jailbreak vulnerabilities.

Understanding, detecting, and preventing hallucinations in AI systems with focus on agentic applications, grounding techniques, and production monitoring.

Behavioural patterns observed across Gemini, GPT, and Claude model families in multi-agent environments, including emotional simulation, hypothesis reification, and cross-family dynamics.

Temperature guidance for 80+ agent roles across 11 categories, from deterministic code generators to creative writers, with rationale for each recommendation.

Fairness, bias detection and mitigation, explainability, interpretability, and ethical AI development practices.

Retrieval-Augmented Generation patterns, vector search, context injection, and hybrid search for grounding LLM responses.

Graph databases, query languages (Cypher, SPARQL), GraphRAG, ontology design, and entity resolution patterns.

Zero-shot, few-shot, chain-of-thought prompting, ReAct patterns, prompt optimization, and security considerations.

LLM evaluation frameworks, benchmark datasets (MMLU, HumanEval), metrics (BLEU, BERTScore), and LLM-as-judge patterns.

LLM inference optimization including sampling parameters, quantization, parallelism strategies, KV-cache, Flash Attention, and throughput techniques.

Tiered judge system that evaluates worker output at the exit of every LLM turn. Structural checks, LLM-powered quality scoring against success criteria, and three verdicts (ACCEPT, RETRY, ESCALATE) controlling graph execution flow.

Vision-language models, audio processing, unified embeddings, and multi-modal architectures for diverse data types.

Neural networks that simulate environments — from playable 3D worlds and game engines running on diffusion models, to photorealistic video generation from text prompts.

Intelligent document processing with OCR, vision-based parsing, table extraction, and RAG integration patterns.

Frameworks, tools, and guardrails for building applications with Large Language Models including prompt engineering and safety measures.

Agent platforms, orchestration frameworks, cloud ADKs, model-agnostic SDKs, and supporting tools across the AI ecosystem.