The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI

This post contributes to the understanding of agent memory and harness engineering by detailing a practical workflow for evaluating and shipping AI agents that work reliably in production. It emphasizes moving beyond 'vibes' to rigorous evaluation, instrumenting agents with observability tools (OpenTelemetry, OpenInference), and establishing clear success criteria. The workflow includes building agents with SDKs (Claude Agent SDK), implementing deterministic checks, creating custom actionability, and addressing production challenges like anchoring bias, context management, causal reasoning, guardrails, and learning loops. The Resolve AI harness provides model orchestration, context engineering, causal reasoning, governed actions, learning systems, and evals.