Building an Advanced Agentic Harness

From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.

In Building a Basic Agentic Harness we borrowed Colonel John Boyd’s OODA loop and built the simplest thing that deserves to be called an agent: a loop that observes its state, asks the LLM to decide, validates the decision, executes a tool, and folds the result back into the state. The LLM is the pilot, and we built the fighter jet around it.

In this post we continue exploring the finer points of Agentic Harnesses and build a full fledge Advanced Harness.

The companion notebook is available on the LLMs for Data Science GitHub repository:

That Basic Harness loop is correct, but naive. A lone pilot in a well-built jet might win a dogfight, but nobody runs an air campaign that way. Real operations add mission planners who decide what sorties to fly before anyone takes off, squadrons that fly independent sorties in parallel, fuel budgets and bingo calls that force a return to base before the tanks run dry, flight recorders that make every mission reconstructible after the fact, and after-act…