https://www.aidaddy.tech AI system design and interview prep for engineers moving into AI from frontend, backend, or data. RAG, agents, MCP, evaluation, and the production patterns you will meet on the job. en-us Fri, 29 May 2026 14:20:47 GMT https://www.aidaddy.tech/learn/00-interview-prep/01-question-bank https://www.aidaddy.tech/learn/00-interview-prep/01-question-bank Fri, 29 May 2026 13:58:08 GMT Interview Prep A topic-organized bank of 110+ AI system design interview questions with model answers, follow-ups, and signals strong candidates show. Updated through May 2026. https://www.aidaddy.tech/learn/01-foundations/01-llm-internals https://www.aidaddy.tech/learn/01-foundations/01-llm-internals Fri, 29 May 2026 13:58:08 GMT Foundations The architectural core of modern LLMs: transformers, MoE, attention math, RoPE, GQA, KV cache, and the inference-optimal scaling shift driving 2026 model design. https://www.aidaddy.tech/learn/01-foundations/02-tokenization-deep-dive https://www.aidaddy.tech/learn/01-foundations/02-tokenization-deep-dive Fri, 29 May 2026 13:58:08 GMT Foundations Tokenization is the process of converting text into discrete units (tokens) that models can process. It directly impacts model capabilities, costs, and performance. https://www.aidaddy.tech/learn/01-foundations/03-attention-mechanisms https://www.aidaddy.tech/learn/01-foundations/03-attention-mechanisms Fri, 29 May 2026 13:58:08 GMT Foundations Attention is the core innovation that enables transformers. This chapter covers the mathematical foundations, variants, and optimizations that are essential for system design and interviews. https://www.aidaddy.tech/learn/01-foundations/04-transformer-architecture https://www.aidaddy.tech/learn/01-foundations/04-transformer-architecture Fri, 29 May 2026 13:58:08 GMT Foundations This chapter provides a comprehensive view of the complete transformer architecture, bringing together the components from previous chapters into a unified understanding. https://www.aidaddy.tech/learn/01-foundations/05-embeddings-and-vector-spaces https://www.aidaddy.tech/learn/01-foundations/05-embeddings-and-vector-spaces Fri, 29 May 2026 13:58:08 GMT Foundations Embeddings are dense vector representations of text that capture semantic meaning. They are foundational to RAG systems, semantic search, and many AI applications. https://www.aidaddy.tech/learn/01-foundations/06-inference-pipeline https://www.aidaddy.tech/learn/01-foundations/06-inference-pipeline Fri, 29 May 2026 13:58:08 GMT Foundations This chapter covers how LLMs generate text at inference time, the computational phases involved, and the key metrics for production serving. https://www.aidaddy.tech/learn/02-model-landscape/01-model-taxonomy https://www.aidaddy.tech/learn/02-model-landscape/01-model-taxonomy Fri, 29 May 2026 13:58:08 GMT Model Landscape This chapter provides a comprehensive guide to the model landscape as of **May 2026**, covering model families, capabilities, and selection criteria for production systems. https://www.aidaddy.tech/learn/02-model-landscape/02-capability-assessment https://www.aidaddy.tech/learn/02-model-landscape/02-capability-assessment Fri, 29 May 2026 13:58:08 GMT Model Landscape This chapter covers how to evaluate and compare model capabilities for your specific use case. Generic benchmarks rarely tell the full story; this guide helps you conduct meaningful assessments. https://www.aidaddy.tech/learn/02-model-landscape/03-pricing-and-costs https://www.aidaddy.tech/learn/02-model-landscape/03-pricing-and-costs Fri, 29 May 2026 13:58:08 GMT Model Landscape Understanding the cost structure of LLM systems is essential for production planning. This chapter covers pricing models, cost optimization strategies, and total cost of ownership analysis. https://www.aidaddy.tech/learn/05-prompting-and-context/07-prompt-optimization-dspy https://www.aidaddy.tech/learn/05-prompting-and-context/07-prompt-optimization-dspy Fri, 29 May 2026 13:58:08 GMT Prompting & Context Prompting has moved from the "Hand-tuning" era to the "Programmatic" era. **DSPy (Declarative Self-improving Language Programs)** is the de-facto standard for building robust LLM pipelines where prompts are optimized automatically by… https://www.aidaddy.tech/learn/06-retrieval-systems/02-chunking-strategies https://www.aidaddy.tech/learn/06-retrieval-systems/02-chunking-strategies Fri, 29 May 2026 13:58:08 GMT Retrieval Systems Chunking is the process of splitting a document into discrete segments for retrieval. Production pipelines have moved beyond blind fixed-size splits to **structure-aware and semantic segments**, with newer techniques like late chunking… https://www.aidaddy.tech/learn/07-agentic-systems/03-tool-use-and-mcp https://www.aidaddy.tech/learn/07-agentic-systems/03-tool-use-and-mcp Fri, 29 May 2026 13:58:08 GMT Agentic Systems Tools are the "hands" of an agent. The industry has standardized on the **Model Context Protocol (MCP)**, which replaces fragmented custom tool definitions with a unified, local-first communication layer. MCP has matured rapidly:… https://www.aidaddy.tech/learn/09-frameworks-and-tools/02-langgraph-orchestration https://www.aidaddy.tech/learn/09-frameworks-and-tools/02-langgraph-orchestration Fri, 29 May 2026 13:58:08 GMT Frameworks & Tools LangGraph is the **de facto standard** for building stateful, multi-agent systems. It reached v1.0 in late 2025 and surpassed CrewAI in GitHub stars in early 2026 thanks to enterprise adoption of its graph-based runtime. Unlike simple… https://www.aidaddy.tech/learn/09-frameworks-and-tools/09-claude-code https://www.aidaddy.tech/learn/09-frameworks-and-tools/09-claude-code Fri, 29 May 2026 13:58:08 GMT Frameworks & Tools Claude Code is Anthropic's **terminal-native autonomous coding agent**. Unlike IDE plugins that suggest completions, Claude Code acts as a full-stack software engineer: it reads your codebase, edits files, runs commands, executes tests,… https://www.aidaddy.tech/learn/09-frameworks-and-tools/10-opencoderguide https://www.aidaddy.tech/learn/09-frameworks-and-tools/10-opencoderguide Fri, 29 May 2026 13:58:08 GMT Frameworks & Tools The AI coding agent landscape has exploded. This guide covers open-weight coding models, agentic IDEs, open-source agents, and how to choose the right tool for your engineering workflow. https://www.aidaddy.tech/learn/09-frameworks-and-tools/11-pydantic-ai-and-mastra https://www.aidaddy.tech/learn/09-frameworks-and-tools/11-pydantic-ai-and-mastra Fri, 29 May 2026 13:58:08 GMT Frameworks & Tools By May 2026 the agent framework debate has stopped being "LangGraph or LlamaIndex." Two newer entrants now own meaningful production share for teams that prioritize type safety over breadth: **Pydantic AI** in the Python world and… https://www.aidaddy.tech/learn/02-model-landscape/04-model-selection-guide https://www.aidaddy.tech/learn/02-model-landscape/04-model-selection-guide Fri, 29 May 2026 13:34:41 GMT Model Landscape A practical framework for choosing the right LLM for your use case, considering capability, cost, latency, and operational factors. https://www.aidaddy.tech/learn/00-interview-prep/07-faq https://www.aidaddy.tech/learn/00-interview-prep/07-faq Mon, 25 May 2026 00:15:53 GMT Interview Prep Short, direct answers to the questions people ask most about modern AI system design. Each answer points to the chapter where the topic is covered in depth. https://www.aidaddy.tech/learn/04-inference-optimization/02-kv-cache-and-context-caching https://www.aidaddy.tech/learn/04-inference-optimization/02-kv-cache-and-context-caching Mon, 25 May 2026 00:15:53 GMT Inference Optimization The KV Cache is the most significant memory consumer in long-context AI systems. Managing this cache effectively is the difference between a system that scales to 2M tokens and one that crashes at 10k. https://www.aidaddy.tech/learn/04-inference-optimization/07-cost-optimization-playbook https://www.aidaddy.tech/learn/04-inference-optimization/07-cost-optimization-playbook Mon, 25 May 2026 00:15:53 GMT Inference Optimization AI costs are no longer "magic." They are measurable, predictable, and highly optimizable. With API pricing down 30-60% over the past year, the cost lever is now mostly about *routing* and *caching*, not just picking a cheaper provider.… https://www.aidaddy.tech/learn/17-tool-use-and-computer-agents/03-openclaw-deep-dive https://www.aidaddy.tech/learn/17-tool-use-and-computer-agents/03-openclaw-deep-dive Mon, 25 May 2026 00:15:53 GMT Tool Use & Computer Agents OpenClaw is an **open-source, self-hosted personal AI agent** that executes tasks through LLMs using messaging platforms as its primary interface. You talk to it via WhatsApp, Telegram, Slack, Discord, or Signal, and it talks back --… https://www.aidaddy.tech/learn/03-training-and-adaptation/01-pretraining-basics https://www.aidaddy.tech/learn/03-training-and-adaptation/01-pretraining-basics Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Pretraining is the most computationally expensive phase of building an LLM, where a model learns general knowledge and language patterns from massive datasets. https://www.aidaddy.tech/learn/03-training-and-adaptation/02-fine-tuning-strategies https://www.aidaddy.tech/learn/03-training-and-adaptation/02-fine-tuning-strategies Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Fine-tuning adapts a pretrained model to specific tasks, domains, or styles. Today, fine-tuning is less about "teaching facts" and more about "teaching format and behavior." https://www.aidaddy.tech/learn/03-training-and-adaptation/03-lora-qlora-peft https://www.aidaddy.tech/learn/03-training-and-adaptation/03-lora-qlora-peft Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Parameter-Efficient Fine-Tuning (PEFT) is the industry standard for adapting LLMs. This chapter covers the mechanics and advanced variants of LoRA and other PEFT methods. https://www.aidaddy.tech/learn/03-training-and-adaptation/04-rlhf-and-dpo https://www.aidaddy.tech/learn/03-training-and-adaptation/04-rlhf-and-dpo Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Alignment is the process of ensuring an LLM's behavior matches human values and instructions. The field has moved from traditional RLHF to more efficient and scalable methods like DPO and Online RL. https://www.aidaddy.tech/learn/03-training-and-adaptation/05-knowledge-distillation https://www.aidaddy.tech/learn/03-training-and-adaptation/05-knowledge-distillation Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Knowledge distillation is the process of transferring the intelligence from a large, complex model ("Teacher") to a smaller, more efficient one ("Student"). This is the secret to the high performance of today's small open-weight models… https://www.aidaddy.tech/learn/03-training-and-adaptation/06-synthetic-data-generation https://www.aidaddy.tech/learn/03-training-and-adaptation/06-synthetic-data-generation Mon, 25 May 2026 00:02:47 GMT Training & Adaptation The industry has hit the "Data Wall", the exhaustion of high-quality human text on the web. Synthetic data is now the primary engine for model improvement, sitting at the core of every modern frontier-model recipe. https://www.aidaddy.tech/learn/03-training-and-adaptation/07-quantization-deep-dive https://www.aidaddy.tech/learn/03-training-and-adaptation/07-quantization-deep-dive Mon, 25 May 2026 00:02:47 GMT Training & Adaptation Quantization is the process of reducing the precision of model weights (e.g., from 16-bit to 4-bit) to save memory and increase inference speed. This is the primary tool for deploying large models on consumer and single-GPU hardware. https://www.aidaddy.tech/learn/04-inference-optimization/01-inference-fundamentals https://www.aidaddy.tech/learn/04-inference-optimization/01-inference-fundamentals Mon, 25 May 2026 00:02:47 GMT Inference Optimization Inference is the process of generating predictions from a trained model. Inference optimization has shifted from "simple speedups" to "architectural efficiency" to handle reasoning-heavy workloads on Hopper (H100) and Blackwell (B200)… https://www.aidaddy.tech/learn/04-inference-optimization/03-speculative-decoding https://www.aidaddy.tech/learn/04-inference-optimization/03-speculative-decoding Mon, 25 May 2026 00:02:47 GMT Inference Optimization Speculative decoding is a now-standard technique that allows large Models (LLMs) to generate multiple tokens per forward pass, effectively breaking the memory-bandwidth bottleneck for sequential decoding. https://www.aidaddy.tech/learn/04-inference-optimization/04-batching-strategies https://www.aidaddy.tech/learn/04-inference-optimization/04-batching-strategies Mon, 25 May 2026 00:02:47 GMT Inference Optimization Batching is the primary lever for increasing LLM throughput and reducing cost. Serving frameworks have moved beyond simple request-level batching to sub-token, iteration-level orchestration. https://www.aidaddy.tech/learn/04-inference-optimization/05-paged-attention https://www.aidaddy.tech/learn/04-inference-optimization/05-paged-attention Mon, 25 May 2026 00:02:47 GMT Inference Optimization PagedAttention is the foundational algorithm behind high-throughput serving engines (vLLM, SGLang, TensorRT-LLM). It solves the "Memory Fragmentation" problem that previously limited LLM scalability. https://www.aidaddy.tech/learn/04-inference-optimization/06-serving-infrastructure https://www.aidaddy.tech/learn/04-inference-optimization/06-serving-infrastructure Mon, 25 May 2026 00:02:47 GMT Inference Optimization Deploying LLMs at scale requires a robust infrastructure layer that handles load balancing, model parallelism, and multi-tenant isolation. The focus has shifted from "serving a model" to "orchestrating an inference fleet." https://www.aidaddy.tech/learn/05-prompting-and-context/01-prompt-engineering-fundamentals https://www.aidaddy.tech/learn/05-prompting-and-context/01-prompt-engineering-fundamentals Mon, 25 May 2026 00:02:47 GMT Prompting & Context Prompt engineering is the design of inputs to steer LLM behavior. It has evolved from "trial and error" to a disciplined architectural practice, with frameworks like DSPy treating it as a compilation problem rather than a writing exercise. https://www.aidaddy.tech/learn/05-prompting-and-context/02-few-shot-and-icl https://www.aidaddy.tech/learn/05-prompting-and-context/02-few-shot-and-icl Mon, 25 May 2026 00:02:47 GMT Prompting & Context In-Context Learning (ICL) is the ability of an LLM to learn a new task simply by seeing examples in the prompt, without any weight updates. Maximizing ICL efficiency is a key lever for prompt stability. https://www.aidaddy.tech/learn/05-prompting-and-context/03-chain-of-thought https://www.aidaddy.tech/learn/05-prompting-and-context/03-chain-of-thought Mon, 25 May 2026 00:02:47 GMT Prompting & Context Chain-of-Thought (CoT) is the technique of encouraging an LLM to generate intermediate reasoning steps before providing a final answer. It has evolved from a simple prompt phrase into the core architectural feature of reasoning models… https://www.aidaddy.tech/learn/05-prompting-and-context/04-tree-of-thought https://www.aidaddy.tech/learn/05-prompting-and-context/04-tree-of-thought Mon, 25 May 2026 00:02:47 GMT Prompting & Context Tree-of-Thought (ToT) is an advanced prompting architecture where a model explores multiple reasoning paths, evaluates them, and "backtracks" if a path leads to a dead end. It is the blueprint behind modern autonomous research agents. https://www.aidaddy.tech/learn/05-prompting-and-context/05-context-engineering https://www.aidaddy.tech/learn/05-prompting-and-context/05-context-engineering Mon, 25 May 2026 00:02:47 GMT Prompting & Context Context engineering is the science of filling the LLM's finite "working memory" with the most valuable tokens. With context windows now reaching 1M+ tokens (Claude Sonnet 4.6, Gemini 3.1 Pro, GPT-5.5) and models gaining Extended… https://www.aidaddy.tech/learn/05-prompting-and-context/06-structured-generation https://www.aidaddy.tech/learn/05-prompting-and-context/06-structured-generation Mon, 25 May 2026 00:02:47 GMT Prompting & Context Structured Generation is the process of forcing an LLM to produce output in a machine-readable format (JSON, YAML, CSV) with 100% reliability. The discipline has moved from "prompt-based requests" to "engine-level constraints." https://www.aidaddy.tech/learn/05-prompting-and-context/08-prompt-injection-defense https://www.aidaddy.tech/learn/05-prompting-and-context/08-prompt-injection-defense Mon, 25 May 2026 00:02:47 GMT Prompting & Context As LLMs become the "operating system" for applications, Prompt Injection is the new "SQL Injection." It is the #1 LLM risk in the OWASP LLM Top 10, and modern defense treats it as an architectural concern, not just a prompt-writing one. https://www.aidaddy.tech/learn/06-retrieval-systems/01-rag-fundamentals https://www.aidaddy.tech/learn/06-retrieval-systems/01-rag-fundamentals Mon, 25 May 2026 00:02:47 GMT Retrieval Systems How RAG evolved from naive vector search to agentic and graph-based retrieval. When to choose RAG vs. long context, and the three retrieval gaps that cause production failures. https://www.aidaddy.tech/learn/06-retrieval-systems/03-embedding-models https://www.aidaddy.tech/learn/06-retrieval-systems/03-embedding-models Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Embedding models convert text into high-dimensional vectors. The frontier has moved past static single-vector representations to **multi-resolution, late-interaction, and multimodal** embeddings. https://www.aidaddy.tech/learn/06-retrieval-systems/04-vector-databases https://www.aidaddy.tech/learn/06-retrieval-systems/04-vector-databases Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Vector databases are purpose-built systems for storing, indexing, and searching high-dimensional embeddings. The market has split into **Managed Serverless** and **Specialized High-Performance** engines. We no longer ask "Does it… https://www.aidaddy.tech/learn/06-retrieval-systems/05-hybrid-search https://www.aidaddy.tech/learn/06-retrieval-systems/05-hybrid-search Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Hybrid search combines dense (semantic) and sparse (keyword) retrieval to get the benefits of both. It is the baseline for production RAG: Elasticsearch's `rrf` retriever, OpenSearch hybrid search, Weaviate, Qdrant, and Azure AI Search… https://www.aidaddy.tech/learn/06-retrieval-systems/06-reranking-strategies https://www.aidaddy.tech/learn/06-retrieval-systems/06-reranking-strategies Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Reranking is the second stage of retrieval that re-scores a small set of candidates (Top 50-100) using a high-precision model. It is the bridge between "efficient search" and "perfect grounding": first-stage retrieval optimizes for… https://www.aidaddy.tech/learn/06-retrieval-systems/07-graph-rag https://www.aidaddy.tech/learn/06-retrieval-systems/07-graph-rag Mon, 25 May 2026 00:02:47 GMT Retrieval Systems GraphRAG is the combination of **Knowledge Graphs (KG)** and **Retrieval-Augmented Generation**. While vector RAG is good at "finding a specific chunk," GraphRAG is designed for **Global Reasoning** across an entire dataset. https://www.aidaddy.tech/learn/06-retrieval-systems/08-agentic-rag https://www.aidaddy.tech/learn/06-retrieval-systems/08-agentic-rag Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Agentic RAG moves from a "Linear Pipeline" to a **"Reasoning Loop."** Instead of retrieving once, an agent decides *when* and *what* to retrieve to resolve a query. The dominant production patterns are Self-RAG (model emits reflection… https://www.aidaddy.tech/learn/06-retrieval-systems/09-advanced-retrieval-patterns https://www.aidaddy.tech/learn/06-retrieval-systems/09-advanced-retrieval-patterns Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Beyond the basics, production RAG systems use specialized patterns to handle complex query-document gaps. These patterns are the "secret sauce" of high-precision search and are increasingly bundled into managed RAG offerings. https://www.aidaddy.tech/learn/06-retrieval-systems/10-contextual-retrieval https://www.aidaddy.tech/learn/06-retrieval-systems/10-contextual-retrieval Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Contextual Retrieval is an ingestion-time technique that solves the #1 cause of RAG failure: **chunks that lose meaning when separated from their source document**. Pioneered by Anthropic in late 2024, it is now a production standard… https://www.aidaddy.tech/learn/06-retrieval-systems/11-late-interaction-colbert https://www.aidaddy.tech/learn/06-retrieval-systems/11-late-interaction-colbert Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Late Interaction is a retrieval paradigm that sits between fast-but-imprecise **bi-encoders** and accurate-but-slow **cross-encoders**. ColBERT (Contextualized Late Interaction over BERT) is the defining model in this space, delivering… https://www.aidaddy.tech/learn/06-retrieval-systems/12-multimodal-rag https://www.aidaddy.tech/learn/06-retrieval-systems/12-multimodal-rag Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Multi-modal RAG extends retrieval-augmented generation beyond plain text to handle images, tables, charts, audio, and mixed-layout documents. Production systems now routinely ingest PDFs with diagrams, slide decks, scanned invoices, and… https://www.aidaddy.tech/learn/06-retrieval-systems/13-rag-evaluation-patterns https://www.aidaddy.tech/learn/06-retrieval-systems/13-rag-evaluation-patterns Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Evaluation is the hardest unsolved problem in RAG. You can build a retrieval pipeline in a day; knowing whether it actually works takes weeks. The industry has converged on a layered evaluation strategy: the RAG Triad for correctness,… https://www.aidaddy.tech/learn/06-retrieval-systems/14-production-rag-at-scale https://www.aidaddy.tech/learn/06-retrieval-systems/14-production-rag-at-scale Mon, 25 May 2026 00:02:47 GMT Retrieval Systems Production RAG is no longer a weekend project. It is a distributed system with retrieval pipelines, caching layers, routing logic, self-correction loops, multi-tenant isolation, and cost controls, all operating under strict latency… https://www.aidaddy.tech/learn/07-agentic-systems/01-agent-fundamentals https://www.aidaddy.tech/learn/07-agentic-systems/01-agent-fundamentals Mon, 25 May 2026 00:02:47 GMT Agentic Systems Agents are LLM-powered systems that move beyond "chat" into "autonomous problem solving." The definition has shifted from simple ReAct loops to **Closed-Loop Reasoning Systems** that use built-in "System 2" thinking (Claude Opus 4.7… https://www.aidaddy.tech/learn/07-agentic-systems/02-reasoning-loops-react-and-beyond https://www.aidaddy.tech/learn/07-agentic-systems/02-reasoning-loops-react-and-beyond Mon, 25 May 2026 00:02:47 GMT Agentic Systems Reasoning Loops define the control flow of an agent. While **ReAct** was the 2023 baseline, current systems use more sophisticated patterns like **Plan-and-Solve**, **Self-Reflexion**, and **Inference-Time Scaling** running on top of… https://www.aidaddy.tech/learn/07-agentic-systems/04-multi-agent-orchestration https://www.aidaddy.tech/learn/07-agentic-systems/04-multi-agent-orchestration Mon, 25 May 2026 00:02:47 GMT Agentic Systems Complex systems are rarely one agent. They are teams of specialized agents. Orchestration has matured from "Blind Managers" to **Hierarchical Supervisors**, **Dynamic Swarms**, and **Cross-Vendor Agent Networks** enabled by… https://www.aidaddy.tech/learn/07-agentic-systems/05-agent-memory-and-state https://www.aidaddy.tech/learn/07-agentic-systems/05-agent-memory-and-state Mon, 25 May 2026 00:02:47 GMT Agentic Systems Memory is what allows an agent to learn and maintain context over time. Agent memory has matured from "Chat History" into a **Multi-Tiered Cognitive Architecture** with four named layers (Working, Episodic, Semantic, Procedural), each… https://www.aidaddy.tech/learn/07-agentic-systems/06-planning-and-decomposition https://www.aidaddy.tech/learn/07-agentic-systems/06-planning-and-decomposition Mon, 25 May 2026 00:02:47 GMT Agentic Systems Planning is the "System 2" component that allows agents to solve multi-stage problems without "wandering." Production agents have moved from simple "Chain-of-Thought" to **Recursive Decomposition** and **Tree Search**, with… https://www.aidaddy.tech/learn/07-agentic-systems/07-error-handling-and-recovery https://www.aidaddy.tech/learn/07-agentic-systems/07-error-handling-and-recovery Mon, 25 May 2026 00:02:47 GMT Agentic Systems Agents fail in non-deterministic ways. Error handling has moved from "Try-Catch blocks" to **Agentic Self-Correction** and **Stateful Rollbacks**, with frameworks like LangGraph and Microsoft Agent Framework providing native…