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Agentic AI

Agentic AI & LLM Engineering

Deep dives into agentic AI, LLM evaluation, synthetic data generation, model selection, and production AI engineering best practices.

Showing 282 of 282 articles

7 min read

Building Autonomous Database Management with AI Agents

How to build AI agents that monitor, optimize, and manage databases autonomously. Covers query optimization, index recommendation, anomaly detection, automated migration generation, and safety guardrails for database operations.

6 min read

Claude API JSON Mode and Structured Output Patterns

Complete guide to getting reliable structured output from Claude. Covers JSON mode, tool-use-as-schema, Pydantic validation, streaming structured data, and error recovery patterns for production applications.

6 min read

Production AI Incident Response: Debugging Rogue Agents

A practical guide to debugging AI agents that misbehave in production. Covers incident classification, root cause analysis patterns, logging strategies, kill switches, and post-incident review processes for agentic AI systems.

7 min read

Building an AI Software Engineer: Lessons from SWE-bench

Analysis of the SWE-bench benchmark for AI coding agents, what it reveals about the state of automated software engineering, and practical lessons for building production coding assistants from the top-performing systems.

6 min read

Building a Self-Healing Codebase with AI Agents

Learn how to build AI-powered systems that automatically detect, diagnose, and fix code issues. Covers CI/CD integration, automated test repair, dependency updates, and real-world self-healing architecture patterns.

6 min read

Claude API Cost Optimization: 8 Proven Strategies

Reduce your Claude API costs by 60-90% with these eight production-tested strategies. Covers prompt caching, model tiering, token budgeting, batch processing, response caching, context compression, and more.

6 min read

Claude Vision API: Analyzing Images and Documents at Scale

Complete guide to using Claude's vision capabilities for image analysis, document processing, and OCR at scale. Covers image formats, multi-image analysis, PDF processing, prompt engineering for vision tasks, and cost optimization.

6 min read

Building a Research Agent with the Claude API

Build an autonomous research agent that searches the web, reads documents, synthesizes findings, and produces structured reports. Covers architecture, tool integration, source verification, and iterative deepening strategies.

6 min read

Building a Code Review Bot with the Claude API

Step-by-step guide to building an automated code review bot using the Claude API. Covers GitHub integration, diff analysis, security scanning, style enforcement, and delivering actionable feedback on pull requests.

6 min read

Building an AI Documentation Assistant with RAG

A complete guide to building a production-grade AI documentation assistant using Retrieval-Augmented Generation, covering chunking strategies, embedding models, vector stores, and answer synthesis.

8 min read

AI Agent Memory Systems: Short-Term, Long-Term, and Episodic Storage

A comprehensive technical guide to implementing memory systems for AI agents, covering working memory (context window management), long-term memory (vector stores and databases), episodic memory (experience replay), and the architecture patterns that make agents truly persistent.

7 min read

Multi-Modal AI in Production: Vision, Audio, and Text Combined

A practical guide to building production multi-modal AI systems that process images, audio, and text in unified pipelines. Covers architecture patterns, model selection, preprocessing, and real-world deployment strategies for multi-modal applications.

6 min read

Claude vs GPT-4o vs Gemini 2.0: Enterprise AI Showdown 2026

A detailed technical comparison of Claude (Anthropic), GPT-4o (OpenAI), and Gemini 2.0 (Google) for enterprise applications in 2026, covering benchmarks, pricing, API features, safety, context windows, and real-world performance across coding, analysis, and reasoning tasks.

7 min read

Mixture of Experts (MoE) Models: How Modern LLMs Scale Efficiently

A technical deep-dive into Mixture of Experts architecture, explaining how MoE models like Mixtral, DeepSeek, and Grok achieve massive parameter counts with efficient inference. Covers routing mechanisms, training strategies, and practical implications for AI engineers.

6 min read

Semantic Caching for LLMs: Cutting API Costs by 60%

Learn how to implement semantic caching for LLM applications to dramatically reduce API costs and latency. Covers embedding-based cache keys, TTL strategies, cache invalidation, and production deployment patterns with Redis and vector databases.

6 min read

LLM Observability: Tracing, Logging, and Debugging AI Systems

A practical guide to implementing observability in LLM applications, covering distributed tracing for multi-step agents, structured logging, cost tracking, quality monitoring, and debugging production issues with tools like LangSmith, Langfuse, and custom solutions.

6 min read

Structured Outputs: Making LLMs Reliably Return JSON

A comprehensive guide to getting reliable structured JSON output from LLMs, covering native structured output modes, Pydantic validation, retry strategies, and production patterns for building robust data extraction pipelines.