A knowledge base that logs daily experiences and learning journeys, sharing them with you to grow together.
This article explains the three core concepts of graph engineering—Node, Edge, and State—then dissects the design philosophies of LangGraph and AutoGen, comparing their architectures, strengths, limitations, and suitable use‑cases to help developers select the right framework without pitfalls.
The article explains that "prompt fatigue" stems from cramming multiple roles and tasks into a single LLM prompt, which causes attention competition and context pollution, and shows how Graph engineering restructures agents into specialized state nodes to isolate context, specialize roles, and enforce controllable workflows, backed by Anthropic’s 90% quality gain at a 15‑fold token cost.
SMEs can adopt lightweight ontologies to improve AI agents in three real-world scenarios—smart customer service, unified sales lead semantics, and inventory‑procurement reconciliation—by explicitly modeling product rules, regional hierarchies, and stock relationships, avoiding hallucinations and heavy platforms while enabling accurate, data‑driven answers.
The article explains why a computer screen itself forms a spatial ontology, how Anthropic trained Claude to compute pixel coordinates and reason over UI elements, the four‑step action loop, benchmark results on OSWorld, prompt‑injection risks, and why combining symbolic and connectionist approaches—neuro‑symbolic AI—is essential for reliable computer‑using agents.
This article explains why Palantir requires ontology binding as the first step for AI agents, describes the OSDK and Ontology MCP toolchain that provide type‑safe access and a standard protocol for external agents, and walks through a minimal agent example with code, permissions, and a key pitfall.
The article analyzes how Microsoft Fabric builds an enterprise semantic layer using ontology—defining entity types, properties, relationships, and data binding—and explains NL2Ontology's natural‑language‑to‑query translation while contrasting this approach with Palantir's decision‑centric model.
After running DeepEval on an AI agent, the article explains how to move from raw metric scores to actionable code changes by reading detailed transcripts, diagnosing root causes, applying quality‑gate thresholds, choosing appropriate pass@k or pass^k metrics, and iterating with CI/CD integration.
The article explains that an AI agent’s “memory loss” is caused by the finite token context window, describes three concrete symptoms—repeating actions, forgetting constraints, and giving contradictory answers—and evaluates three engineering solutions (sliding‑window truncation, context compression, and external memory) with their trade‑offs, plus practical tips such as using CLAUDE.md for persistent rules and session_id for resume.
This article walks through the full lifecycle of a user message in Claude Code's Agent SDK, explaining each processing stage, the crucial tool‑use decision, parallel tool execution, error handling, and how Hooks let developers extend the loop without modifying core logic.
This tutorial walks through a minimal 50‑line Python implementation of an AI Agent Loop, covering the core four‑step cycle, dual termination strategies, deterministic vs. autonomous designs, tool registration, and a complete runnable example.
