Episodic memory is what lets an agent remember what happened in past sessions. This part builds a complete production episodic memory system using PostgreSQL with pgvector, implementing hybrid time-based and semantic retrieval in Node.js so your agent never starts from zero again.
Category: AI
AI Agents with Memory: Why Single-Session Agents Fail in Enterprise and the Three Memory Types That Fix It
Most agent guides cover single-session work. Enterprise agents need persistent memory across sessions. This first part explains why stateless agents break down at enterprise scale, introduces the three memory types that solve it, and maps out the architecture this series will build.
Production Monitoring for LLM Caching: Cache Hit Rate Dashboards, TTFT Measurement, and ROI Calculation
Shipping caching without monitoring is flying blind. This final part covers how to build cache hit rate dashboards, measure time-to-first-token improvements, calculate real cost savings with accuracy, detect cache regression before users notice, and build the business case for continued caching investment.
Agentic AI in 2026: How Autonomous Systems Are Reshaping Enterprise Technology
Gartner projects 40 percent of enterprise applications will embed AI agents by end of 2026. This post covers the agentic AI shift, MCP hitting 97 million installs, the April 2026 frontier model landscape, OS-level AI integrations, and the governance gap enterprises must close.
Multi-Provider AI Gateway in Node.js: Unified Caching, Routing, and Fallback for Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro
A unified AI gateway abstracts over provider-specific caching implementations, routing logic, and fallback handling. This part builds a production-ready Node.js gateway that handles Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro transparently, with cross-provider cost tracking and cache hit monitoring.
Context Engineering Strategies: Designing Prompts for Cache Efficiency, RAG Pipelines, and Production Scale
Context engineering is the discipline of designing what goes into your LLM context window, in what order, and how to structure it for maximum cache efficiency, retrieval quality, and cost control. This part covers static-first architecture, cache-aware RAG design, prompt versioning, and token budget management.
Semantic Caching with Redis 8.6: Vector Similarity Matching for LLM Cost Optimization in Production
Semantic caching operates above the model layer, using vector embeddings to match similar queries to previously computed responses. With Redis 8.6, you can achieve 80 percent or higher cache hit rates without calling the LLM at all. This part covers the full architecture, similarity thresholds, cache invalidation, and production implementations in both Node.js and Python.
Context Caching with Gemini 3.1 Pro and Flash-Lite: Implicit vs Explicit Caching, Storage Costs, and Python Production Implementation
Google Gemini 3.1 Pro and Flash-Lite offer both implicit and explicit context caching, with the most generous default TTL of any major provider at one hour. This part covers how both modes work, how to account for storage costs, and a complete Python production implementation for Vertex AI and the Gemini API.
Prompt Caching with GPT-5.4: Automatic Caching, Tool Search, and C# Production Implementation
GPT-5.4 makes prompt caching automatic with no configuration required. This part covers how OpenAI’s caching works under the hood, how to structure prompts for maximum hit rates, how the new Tool Search feature reduces agent token costs, and a full production C# implementation with cost tracking.
Prompt Caching with Claude Sonnet 4.6: cache_control Breakpoints, TTL Strategies, and Node.js Production Implementation
Claude Sonnet 4.6 gives developers explicit control over prompt caching through cache_control breakpoints. This part covers how to structure your prompts, configure TTL, use multi-breakpoint strategies, and implement a production-ready caching layer in Node.js.
