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Agentic RAG systems combine: ●       Reasoning models  ●       Retrieval systems ●       Tool use (increasingly via MCP) ●       Memory (short-term, long-term, and now tiered/benchmarked) ●       Workflow orchestration ●       Governance controls ●       A har...

Control > Convenience: Explicit systems outperform hidden automation. Context > Prompting: Prompt engineering helps. Context engineering scales,  it is now treated as the discipline of designing everything a model sees on every inference call, not just the ins...

Agentic systems are AI systems that can: ●       Maintain internal state ●       Plan and decompose tasks ●       Use tools ●       Retrieve knowledge ●       Reflect and self-correct ●       Operate under constraints ●       Escalate to humans when needed Con...

The diagram presents AI as a progression of interconnected layers, from traditional rule-based systems to increasingly autonomous and goal-driven AI. Here is a paragraph explaining each layer: 1. Classical AI Classical AI represents the foundational approache...

The AI Factory Stack illustrates the key components required to transform a general-purpose Large Language Model (LLM) into a reliable, production-ready AI system. Each layer adds a specific capability, moving from the core intelligence of the model to ...

The Modern AI Ecosystem – Tools diagram presents the modern AI landscape as a collection of interconnected layers and capabilities. Rather than being built around a single model or platform, modern AI systems combine specialized tools for intelligence, dat...

NEW IN 2026  Harness engineering emerged through 2026 as a distinct discipline, formalized in surveys covering 100+ papers and 23 production systems. The core claim: at fixed model capability, agent-computer interface design materially changes benchmark outcom...

Six Harness Components ●       State and persistence — durable memory and workflow checkpoints across long-running runs. ●       Security and governance — permissioning, sandboxing, credential scoping. ●       Orchestration and tool routing — how planner/execu...

Large Action Models (LAMs) Models designed to reason about actions, workflows, and consequences. Used for: ●       Browser automation ●       Enterprise workflows ●       Multi-step operations ●       Transaction execution Model Specialization Role Bes...

Layer Recommended Options LLMs LLaMA 4, Qwen 3, Mixtral, DeepSeek Fast Inference Groq, vLLM, Ollama Agent Framework LlamaIndex Workflow Engine LlamaIndex Workflows API Layer FastAPI Vector DB Qdrant, Chroma Graph ...

1. User Request The process begins when a user submits a request to the AI system. This request could be a question, instruction, or task that the user wants the system to perform. At this stage, the request represents the user's intent, but it has not yet be...

Many failures are context failures, not model failures. This remains true, and is now the organizing principle of the discipline described in Section 5. Context Builder Responsibilities Assemble: ●       Relevant memory ●       Retrieved evidence ●       Tool ...