From a single LLM call to a production agent: planning, parallelism, memory, verification, and budgets

Building an Advanced Agentic Harness

This post upgrades a naive agent loop into a production-shaped system step by step. Using a city comparison task, it adds Pydantic-typed tools to catch invalid arguments early, a DAG-based plan so nine independent lookups run in parallel, and tiered memory with a retrieval budget to keep the context window clean. Output quality is guarded by splitting prompts into Planner, Worker, and Critic roles plus a verification hierarchy, while multi-dimensional budgets handle cost pressure with graceful degradation. Everything is built as small, testable primitives without a framework, and a MockProvider makes the whole setup reproducible offline.

Why it matters: A substantive agent engineering piece with concrete, copyable techniques for validation, parallelism, memory, and verification. Docked slightly because the author/platform isn't a tier-1 lab, and the purely engineering angle lacks an emotional hook.