Guides, engineering and product updates
Blog & Updates
How to build, run and watch Coworkers: practical guides, engineering deep dives and product updates from the team behind Rerun.
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Engineering articles.
Agentic Design Patterns: The Complete Guide to Building Reliable AI AgentsA 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 CommunicateThe 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.
Agentic RAG: How Agents Supercharge Retrieval-Augmented GenerationNaive 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 MattersAI 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.
What Is the Model Context Protocol (MCP)? A Complete GuideThe 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.
ReAct Agents: How the Reasoning-and-Acting Loop Actually WorksReAct 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 AgentsLearn 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 Agent Guardrails: LLM Guardrails for Safe Production AgentsAI 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.
AI Agent Architecture Explained: Components, Patterns & Control PlaneA 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.
Human-in-the-Loop AI Agents: The Complete Guide to Agents You Can DeployHuman-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.
What Is the Hermes AI Agent? How It Works, Whether It's Safe, and How to Run ItHermes 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.
AI Agent Security: Risks, Best Practices & GuardrailsAI 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.2,000+ businesses run on Rerun
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