Agents

Autonomous software development works when agents write software in a loop: scoped tasks, selected context, sandboxed work, verification, review, and memory.

Context graphs turn agent activity into a durable record of decisions, exceptions, approvals, and precedent. That may be the next enterprise system of record.

HTML is unusually effective as an agent review surface when the output needs layout, interaction, or visual inspection, while Markdown remains the durable source format.

Production agents get cheaper and more reliable when teams treat context as infrastructure, not as an ever-growing prompt.

A practical framing of /goal as the objective and evidence layer for coding agents and Hermes-style assistants.

What an LLM Wiki is, why it changes agent knowledge work, and how to build one with scoped topics, source manifests, page types, writeback, staging, search, and linting.

A deployment-focused read of Gemma 4: why E2B, E4B, 26B A4B, and 31B dense are best understood as a model-selection ladder for edge and agent workloads.

DESIGN.md gives AI coding agents a portable design contract: tokens, rationale, component rules, and constraints that help generated interfaces preserve a product’s visual identity.

Agent Skills are a portable, open-standard format for packaging procedural knowledge so any AI agent can load domain expertise on demand—turning a general-purpose model into a specialist without re-explaining workflows every time.

Prompt engineering describes the recipe; context engineering manages the kitchen. A technical playbook for the "mise en place" of AI agents: Write, Select, Compress, and Isolate.

The Model Context Protocol (MCP) is a standardized framework for integrating AI systems with diverse data sources and Applications. This post explores MCP’s architecture, core components, and best practices.

Explore the six essential elements that make multi-agent systems effective: Role Playing, Focus, Tools, Collaboration, Guardrails, and Memory. Learn how specialized agents working together can outperform single-agent solutions through clear roles, focused responsibilities, and powerful collaboration patterns.

Explore ReAct, a framework where language models observe, reason, and act in a continuous cycle. Learn how this three-step process enables AI to gather information, think through problems step-by-step, and take concrete actions - creating more capable and reliable AI systems that can adapt their approach based on real-world feedback.

MemGPT revolutionizes LLM capabilities by implementing operating system-like memory management, enabling persistent context and long-term learning across conversations.