Agent harness engineering: patterns for production loops, memory, and MCP servers

A resource collection for AI agent harness engineering has circulated among practitioners this week, compiling architecture patterns, agent loop designs, and memory systems used across major labs and frameworks. The collection targets teams building production agent systems and covers verification pipelines, Model Context Protocol (MCP) servers, and multi-agent orchestration.

Harness engineering refers to the scaffolding around a model — the code that handles tool calls, memory persistence, verification steps, and coordination between multiple agents. As agent deployments move from demos to production, these patterns have become a bottleneck for reliability and cost control.

What stands out

1. 01Agent loop templates — reusable patterns for ReAct-style loops, chain-of-thought prompting with tool use, and reflection cycles that let agents critique their own outputs before committing to an action.
2. 02Memory systems — architectures for short-term context management, long-term vector stores, and hybrid approaches that balance latency with retrieval quality. Includes examples of when to use semantic search vs. recency filters.
3. 03Verification and sandboxing — code for output validation, constraint checking, and isolated execution environments. Covers both rule-based checks (schema validation, type safety) and model-based verification (a second model reviews the first model's output).
4. 04MCP servers — reference implementations of Model Context Protocol servers, the Anthropic-backed spec for exposing tools and data sources to agents in a standardized way. Includes examples for file systems, databases, and API wrappers.
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