Episodic memory grows indefinitely. Without consolidation, retrieval degrades and storage costs climb. This part builds a Node.js background worker that compresses episodic memory into semantic knowledge on a rolling schedule, keeping your agent sharp without ballooning your database.
Category: AI
AI Agents with Memory Part 4: Procedural Memory – Agents That Learn From Past Actions Using C#
Episodic memory records what happened. Semantic memory stores what is known. Procedural memory stores how things are done. This part builds a production procedural memory system in C# that records successful tool sequences and problem-solving patterns so agents get measurably better with every session.
The April 2026 AI Report: Model Wars, a 100x Energy Breakthrough, and the EU AI Act Countdown
A comprehensive look at the biggest AI stories of April 2026: the model leaderboard reshuffle driven by Claude Opus 4.6 and Gemini 3.1 Ultra, a 100x energy efficiency breakthrough from Tufts University using neuro-symbolic AI, the August 2026 EU AI Act compliance deadline bearing down on enterprises, and major infrastructure bets from Anthropic, Snowflake, and Microsoft.
AI Agents with Memory Part 3: Semantic Memory – Building a Long-Term Knowledge Layer with Qdrant and Python
Episodic memory records what happened. Semantic memory stores what the agent has learned. This part builds a production semantic memory layer using Qdrant and Python, with fact extraction, importance-weighted upserts, and similarity retrieval that lets agents build genuine knowledge about users and domains over time.
AI Agents with Memory Part 2: Episodic Memory – Storing and Retrieving Conversation History at Scale with PostgreSQL, pgvector, and Node.js
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.
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.
