AI Agent Infrastructure: The Runtime & Serving Stack
AI agent infrastructure is the runtime and serving stack that keeps agents safe in production. The layers, the control plane, and why 40% of projects fail.
Read articleBlog & Updates
How to build, run and watch AI agents. Guides, engineering deep dives and product updates from the Rerun team.
LangChain vs LangGraph: Which AI Agent Framework Should You Use in 2026?
LangChain vs LangGraph, explained by engineers: they're from the same company. Learn which layer your AI agent needs in 2026, when to use both, and how to run either safely in production.
AI Agents for Insurance: Use Cases, Risks, and How to Deploy Them Safely
AI agents can triage claims, support underwriting, and flag fraud, but insurance is a high-risk, regulated vertical. Here are the real use cases and the governance-first architecture (human-in-the-loop approvals, least privilege, monitoring, audit trails) that make them safe to deploy.
Agentic Design Patterns: The Complete Guide to Building Reliable AI Agents
A practical catalog of agentic design patterns: Reflection, Tool Use, Planning, and Multi-Agent, plus the five workflow patterns, a decision framework for choosing between them, and how to run them in production.
A2A Protocol: How Autonomous Agents Communicate
The A2A protocol is the open standard for agent-to-agent communication. Here is how it works, how it differs from MCP, and what it means for building and governing multi-agent systems in production.
Claude Code vs Codex: A Hands-On 2026 Comparison of Two Coding Agents
Codex vs Claude Code, compared for real. Local terminal vs cloud sandbox, code quality vs autonomy, real pricing, and the orchestration layer neither CLI gives you.
AI Agents for Accounting: What They Do, Where They Break, and How to Deploy Them Safely
AI agents can now run real accounting work, from reconciliations to month-end close. Here is what they do, where they break, and how to deploy them with approvals, audit trails, and least-privilege access.
Agentic RAG: How Agents Supercharge Retrieval-Augmented Generation
Naive RAG retrieves once and hopes. Agentic RAG puts an agent in charge of retrieval: it plans, routes across sources, grades results, and re-retrieves until it has enough context. Here is how it works and how it beats classic RAG.
AI Agent Governance: What It Is and Why It Matters
AI agent governance controls what autonomous agents can access and do, enforced at runtime, not just on paper. Here are the five pillars of a real framework and how to close the gap between policy and enforcement.
n8n vs Zapier: The Best Automation Platform for AI Workflows (2026)
A data-backed n8n vs Zapier comparison for 2026: pricing, integrations, and AI workflows. Plus the one question both tools cannot answer, and when to reach for a governed AI agent instead.
AI Agents for Data Analysis: From Demos to Production
AI agents for data analysis are easy to demo and hard to trust in production. Here is what they do, where they break, and how to run one you can actually watch and govern.
How to Build an AI Agent (That You Can Actually Trust in Production)
A step-by-step guide to building an AI agent: the model, tools, memory, and reasoning loop, plus the guardrails, approvals, and observability that make it production-ready.
AI Agent Frameworks Explained: How to Choose the Right Foundation
A framework gives your agent a brain and a loop. It does not give you governance, human control, or observability. Here is how to choose an AI agent framework, and what you need around it to run one in production.
AutoGen vs CrewAI: Which Multi-Agent Framework Should You Build On in 2026?
AutoGen is in maintenance mode, CrewAI went standalone. A 2026 comparison of architecture, orchestration, and production readiness, plus the governance layer both leave to you.
AI Agents for HR and Recruiting: Screening, Onboarding, and Automation (Done Safely)
AI agents can now run most of the HR lifecycle, from candidate screening to onboarding to employee support. But recruiting is legally high-risk, so the deployment model matters more than the model. Here is what HR agents actually do, and how to deploy them without creating bias, privacy, and compliance exposure.
What Is the Model Context Protocol (MCP)? A Complete Guide
The Model Context Protocol (MCP) is the open standard connecting AI apps to tools and data. Learn how MCP works, its architecture, primitives, transports, and security, plus how to run MCP servers safely.
AI Agent Memory: How Agents Store, Retrieve, and Learn
How AI agent memory works: short-term vs long-term, the episodic, semantic, and procedural types, how agents store and retrieve with vector search, how they learn, and how to govern memory in production.
CrewAI Alternatives: 7 Governed No-Code AI Agent Platforms Compared (2026)
Gartner says 40% of agentic AI projects will be canceled by 2027, mostly over governance. Here are 7 CrewAI alternatives compared on human-in-the-loop, observability, and audit, from frameworks to governed no-code platforms.
AI Agent Cost: Pricing Models, Token Optimization & the ROI Math That Actually Matters (2026)
What AI agents really cost in 2026: the five pricing models, the hidden token spend that dwarfs the sticker price, and the ROI math to justify buy vs. build.
ReAct Agents: How the Reasoning-and-Acting Loop Actually Works
ReAct agents solve tasks by looping through Thought, Action, and Observation. Here is how the reasoning-and-acting loop works, a real worked example, how it compares to Chain-of-Thought and function calling, and why the raw loop needs a control plane in production.
AI Agent Testing: How to Build a Test Harness for Reliable Agents
Learn how to test AI agents that actually work: golden datasets, LLM-as-a-judge, trajectory evals, and the runtime governance that turns testing into reliability.
AI Agents for Healthcare: Use Cases, Risks, and How to Deploy Them Safely
AI agents can take real work off clinical and admin teams, from scheduling to prior authorization to ambient documentation. The hard part is not the use cases, it is deploying them safely under HIPAA and human oversight. Here is how.
OpenClaw vs OpenCode: Which Open-Source AI Agent Should You Actually Use? (2026)
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.
Types of AI Agents Explained: The 5 Core Categories
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.
Hermès Agent vs OpenAI Codex: Autonomous Orchestrator or Code Engine? (2026)
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.
AI Agents for Real Estate: How to Deploy Agents That Actually Close the Loop (2026 Guide)
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 vs n8n: Autonomous Agents vs Visual Workflows (2026)
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: Autonomous Agent or Workflow Automation? (2026 Guide)
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: LLM Guardrails for Safe Production Agents
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.
LangGraph vs CrewAI: How to Choose an AI Agent Framework (2026)
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.
Hermes AI Pricing: The True Cost of Running Hermès Agent in 2026
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.
AI Agents for Finance: Use Cases, Workflows, and How to Deploy Them
How finance teams deploy AI agents on close, AP, and FP&A workflows safely, with human approvals, least-privilege access, and full observability.
OpenClaw vs Claude Code (2026): Different Jobs, Very Different Risk Surfaces
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.
AI Agent Architecture Explained: Components, Patterns & Control Plane
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.
Hermes AI Agent vs Claude Code: Persistent Daemon vs Session Coding CLI (2026)
Hermes is a persistent, always-on AI daemon; Claude Code is a session-based coding tool. Here is the honest comparison, when to use which, and why the best 2026 setup runs both under a governance layer.
Human-in-the-Loop AI Agents: The Complete Guide to Building Agents You Can Actually Deploy
Human-in-the-loop AI agents pause on high-stakes actions to get human approval. Here are the approval gates, confidence thresholds, and escalation patterns that make agents production-ready.
Hermes vs OpenClaw (2026): Honest Comparison + When to Skip Self-Hosting Entirely
An honest, head-to-head comparison of Hermes Agent and OpenClaw across architecture, memory, security, setup, and cost, plus when self-hosting either agent is the wrong call.
Agentic AI vs. AI Agents: What's Actually the Difference?
Agentic AI and AI agents are not the same thing. One is an entity, the other a paradigm. Here is the real difference, why it matters in production, and how to govern the autonomy that comes with it.
What Is the Hermes AI Agent? How It Works, Whether It's Safe, and How to Run It
Hermes Agent is the open-source, self-improving AI agent by Nous Research. Here is what it is, how it works, whether it is free and safe, and where the line sits between running an agent and governing one in production.
How to Deploy AI Agents in Production: A Step-by-Step Guide
A governance-first, step-by-step guide to deploying AI agents in production, plus the four pillars that separate a demo from an agent you can trust: approvals, observability, least privilege, and secure hosting.
The Best Hermes AI Alternative in 2026: Governed, Human-in-the-Loop AI Agents
Looking for a Hermes AI alternative? We rank and compare Hermes Agent, OpenClaw forks, Claude Code, Lindy and Rerun on security, control, and governance, and explain why a human-in-the-loop agent wins.
AI Workflow Automation: Use Cases, Tools, and How to Get Started (2026)
AI workflow automation runs multi-step work with agents that reason and act, not rigid if-this-then-that rules. Here are the real use cases, how the tools differ, and how to get started.
The Best OpenClaw Alternatives in 2026 (Safer, No-Code, Human-in-the-Loop)
OpenClaw is powerful, but it runs with full system access and no approval step. Here are the best OpenClaw alternatives in 2026, ranked for safety, visibility, and no-code use.
AI Agent Security: Risks, Best Practices & Guardrails
AI agents don't just answer, they act. Here are the 8 biggest AI agent security risks, the guardrails that contain them, and how to build an agent that is secure by design.
AI Agent Orchestration: How to Coordinate Multi-Agent Systems
AI agent orchestration coordinates multiple reasoning agents toward one goal. Learn the four patterns, the architecture, and how to run a multi-agent system with no code.
AI Agents for Customer Service: Use Cases, Examples & Best Tools (2026)
AI agents for customer service do not just reply, they take action: look up the order, issue the refund, update the CRM. Here are the real use cases, company examples, and the best tools to buy or build in 2026.
AI Agent Examples: 15 Real-World Use Cases (2026)
15 real-world AI agent examples in production today, grouped by function, each with the task it owns and the outcome it drives. No thermostats, no hype, plus how to build your own without code.
AI Agents for Small Business: What They Do and the Ones Worth Paying For (2026)
AI agents for small business, tested: what they do, the jobs they handle best, real pricing, and how to pick one. Plus free options and setup in a weekend.
AI Agents for Marketing: Top Use Cases and Tools in 2026
What AI agents for marketing actually do, the top use cases and tools in 2026, and how non-technical teams can build their own autonomous agent, no code required.
AI Agents for Business: What They Actually Do (and How to Build Your Own)
Most businesses are either talking to chatbots or wiring up flowcharts. Here is what AI agents actually do, where they beat every other tool, and how to build your first one in under an hour.
Agentic AI vs. Generative AI: What's the Real Difference?
Agentic AI pursues goals. Generative AI responds to prompts. Here are the 5 key differences, real side-by-side examples, and a plain-English decision guide for 2026.
AI Agent vs. Chatbot: Why They're Not the Same (And Why It Matters)
Chatbots talk. AI agents act. Zapier-style tools? Neither. Here is the real difference between the three categories and how to pick the right one for your workflows.
Best No-Code AI Agent Builders in 2026: Ranked and Tested
We tested the top no-code AI agent builders of 2026. See which tools actually let you build, deploy, and monitor autonomous agents, no code required.
Autonomous AI Agents & Agentic AI: How to Build and Deploy Them (2026 Guide)
A practical 2026 guide to building and deploying autonomous AI agents — covering architecture, frameworks, memory, tool use, production deployment, and cost controls.
AI Agent Observability: Monitor and Debug in Real Time
Most AI agents fail not because they were built wrong, but because no one could see what was happening. Here is how AI agent observability helps you monitor, trace, and debug in real time before a silent failure becomes a production incident.
Best AI Coding Agents in 2026: GitHub Copilot, Cursor, Devin and More Compared
The definitive 2026 guide to AI coding agents: GitHub Copilot vs Cursor vs Devin vs Devin Desktop vs Amazon Q Developer. Feature matrix, real pricing, and what comes after the code.
How to Build an AI Sales Agent Without Code (That Actually Closes Leads)
Build an AI sales agent that qualifies inbound leads, writes personalized replies, and books discovery calls. Step-by-step guide using Rerun. No code required.
© 2026 Rerun. All rights reserved.