@mardehaym: To truly understand AI agents, you need to understand the harness. And it's not the model. I went deep on how a working…

To truly understand AI agents, you need to understand the harness. And it's not the model. I went deep on how a working agentic system actually gets assembled, and it clicked. Here are the notes: Easy Mode: WTF is a harness - The harness is everything wrapped around the model that turns a chatbot into an agent that does real work - An agent is not a model. It's a controlled workflow: trusted context + bounded tools + evaluation + human judgment + operational ownership - The model is the smallest, most swappable part. The harness is all the rest - Recipe is simple: instructions + scoped context + tools + a verifier + guardrails - Example: a research agent that answers an operational question. It confirms who's asking, plans a bounded analysis, pulls only approved data, runs trusted calculations, cites its evidence, and stops when confidence is too low - That's the whole point of a harness. It makes the answer traceable and safe instead of plausible prose Hard Mode: WTF is actually inside it - A working agent sits inside 6 layers, each answering one production question - Trigger: work starts from an event (a file lands, a message arrives, a schedule fires), not a human pressing a button - Orchestration: the loop, memory, retries, loop limits. How far the agent is allowed to go is a config setting, not code - Tools: numbers that must never vary run as fixed logic behind the agent, so it returns the same figure every time. The model doesn't improvise your KPIs - Trusted context: where truth lives and what the agent is allowed to see. This is ~80% of agent success. Context quality is the ceiling, not model power - Control: golden sets, guardrails, approvals. The agent advises, a named person decides - Runtime: traces, cost dashboards, audit. Once this exists, a model swap is just configuration God Mode: WTF makes the harness the moat - The durable asset is the harness: workflow knowledge, tools, context, evals, controls. Models and platform services change underneath it - Hyperscalers give you the foundation (hosting, identity, networking). They don't give you your business-specific harness. That's the source of advantage - Improvement is a loop: real production runs, corrections become new tests, the eval set and the agent both get sharper - Harness engineering is the cheap place to start: tweak prompts, tool definitions, model choice, model combos, long before you fine-tune anything - Traces are the receipts. See a bad outcome, read the trace, find the wrong step, change the harness so it never repeats - Highest-leverage act in the whole thing: a human writing down what good looks like. Everything else compounds on top of that The model gets the hype. The harness gets the results.