The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI
Source
youtube.com
Author
AI Engineer
Date
Why it matters
Production incident agents fail differently from coding agents. The six-pillar agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → (orchestration, context, causal reasoning, governed actions, learning, evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition →) gives a checklist for building agents that must produce one correct root cause.
Key takeaways · AI-distilled
Krovvidi claims about 70% of engineering time goes to running and fixing software rather than writing it.
His explanation for the gap: code is self-documenting, modular and one domain, while production spans code, infrastructure, telemetry and many teams and needs one correct answer.
The five failure modes he lists: anchoring bias and the model treadmill, too much or too little context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, answers that are coherent but not causal, missing guardrailsThe checks around a model that block bad inputs and outputs — filters, validators, and permission rules the model itself can't override.Full definition →, and no learning across investigations.
Terms in this piece · Glossary
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
guardrails — The checks around a model that block bad inputs and outputs — filters, validators, and permission rules the model itself can't override.
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