Sharing AI knowledge, practical implementation records, and more.
The article analyzes how Hermes Agent extends the OpenClaw architecture by embedding persistent memory, procedural skill storage, and cross‑session identity into the agent lifecycle, enabling multi‑day, multi‑entry collaboration that ordinary stateless agents cannot sustain.
The article explains how durable execution, exemplified by Temporal’s Workflow and Activity model, transforms long‑running Agent tasks—such as refund approvals that involve human sign‑off, external APIs, and overnight processing—into recoverable, auditable pipelines that survive crashes, restarts, and timeouts.
The article explains how production‑grade AI agents require a full‑stack security framework—covering input sanitization, runtime policy enforcement, output verification, and audit—to mitigate ten OWASP attack surfaces such as prompt injection, tool misuse, memory poisoning, and cascading failures, with practical defense layers and red‑team testing guidance.
The article explains why single‑role prompts limit large language model depth and introduces a dynamic multi‑expert aggregation prompting method that first performs a neutral diagnosis, generates complementary experts, conducts structured debate, and aggregates results through NGT, producing comprehensive, actionable solutions for complex problems.
The article explains why traditional logs are insufficient for LLM agents, outlines five observability dimensions—tracing, metrics, behavioral governance, state & memory, and evaluation—and provides concrete, open‑source‑based steps to instrument, monitor, and act on agent workloads in production.
When a single AI agent’s logic hits bottlenecks, the article explains how breaking responsibilities into bounded microservice agents, using pipelines for deterministic steps and supervisors for dynamic routing, yields clearer contracts, shared state, easier debugging, and more stable, scalable task execution.
The article explains why relying solely on handcrafted prompts leads to hallucinations in LLM agents and presents six concrete context‑engineering practices—XML isolation, hierarchical ordering, KV caching, vector reranking, async memory compression, and minimal few‑shot examples—illustrated with a full e‑commerce refund‑handling case study.
The article explains that simply scaling large language models cannot sustain long‑running, production‑grade AI agents, and that a dedicated Agent Harness—acting as an operating system with orchestration, memory, governance, tool execution, and feedback loops—is essential for reliable, industrial‑scale automation.
The article explains why routing every request to the most powerful LLM hurts cost, speed, and throughput, and presents a three‑layer task decomposition that assigns execution‑level tasks to cheap small models, intermediate tasks to mid‑size models, and high‑risk judgment tasks to large models, with concrete examples and a minimal routing strategy.
The article explains how to avoid the extremes of fully automated AI or no AI at all by defining clear Human-in-the-Loop patterns, identifying irreversible, high‑responsibility, and high‑exception steps, and applying tailored approval, edit, and escalation nodes in finance, contract, and other critical business processes.
