How to build, run and watch AI agents: practical guides, engineering deep dives and product updates from the team behind Rerun.
OpenClaw and OpenCode are not really competitors. One is a synchronous coding agent, the other a self-hosted personal assistant. Here is how to choose, when to use both, and how to run either safely in production.
The five core types of AI agents, from simple reflex to learning agents, ordered by autonomy. Real examples, where agentic and multi-agent systems fit, and how to match the type to the job.
Codex writes code on demand. Hermès Agent orchestrates and governs it 24/7. A 2026 decision framework, plus how to drive Codex from inside Hermès.
68% of Realtors use AI, but 46% see no impact. AI agents for real estate qualify leads, book showings, and update your CRM, not just chat. Here's how to deploy governed, compliant agents step by step.
OpenClaw runs autonomous agents; n8n runs visual workflows. Neither ships production governance. Here is how the governed middle ground beats both in 2026.
Hermes Agent vs n8n compared honestly: an autonomous AI agent versus a workflow automation platform. Execution model, memory, error handling, governance, and why smart teams pair them.
AI agent guardrails are the runtime controls that keep autonomous agents safe. Learn the 6 guardrail types, where to enforce them, and how to ship agents you can actually trust.
CrewAI vs LangGraph compared across architecture, state, human-in-the-loop, observability and production readiness, plus when to pick each and how to govern both in production.
Hermès Agent is free and open-source, so why do real bills range from $8 to $200+ a month? A full breakdown of hosting, tokens, and the hidden ops costs, plus the transparent managed alternative.
How finance teams deploy AI agents on close, AP, and FP&A workflows safely, with human approvals, least-privilege access, and full observability.
OpenClaw and Claude Code barely compete. One is a sandboxed coding agent, the other an always-on assistant with full system access across 29 channels. A per-task decision framework, the real risk surfaces mapped to OWASP, and how to run an always-on agent safely.
A practitioner's guide to AI agent architecture: the core components, the perceive-reason-act loop, single vs multi-agent patterns, and the production control plane most designs miss.
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