If you use Claude or ChatGPT, you’ve probably tried it: drop in the project docs, ask for an estimate or a design, and see what comes back. And this year, for the first time, it actually works. Models are finally smart enough, with context windows big enough to handle jobs that size with little hand-holding. But the model isn’t doing it alone. Behind that chat window is an agentic harness, and it’s doing more of the work than you might think.
An agentic harness wraps an AI model in the software and runtime it needs to stop being a stateless text generator and start working like an autonomous agent.
When you request the model to create a proposal, or perform a line diagram, that agent is spinning up a virtual environment and writing a program to build your request. Each time you perform this task, the model must perform this monstrous task, and each time it may be a little different, resulting in different results each time. The skills for how to create that program aren't the issue, its the new models interpretation of those skills, and the massive amount of tokens required to rebuild a program each on the virtual environment each times it's needed. A purpose built harness solves this issue, from a repeatability standpoint, and from a token savings standpoint. Next we will cover how a custom harness which contains the virtual environment and programs which are always available and ready to run will result in robust behavior with reduced token spend, making it an excellent base layer for production level work.
Know that the quality of the harness sets the ceiling on the work. A general-purpose harness like a chat app can get you a draft, but the project lives in a conversation, and every output is text the model improvises on it's own, providing good results today, bad tomorrow. AVstackr, or any other purpose built framework gives the agent a workspace with direct access to your catalog, schematics, rack elevations, and BOMs, inside a persistent project where decisions recorded, rather than forgotten, and every output is a control validated object, one that takes shape in a predetermined schema, making for reliable output each time.
That difference between work that’s production-ready out the gate and work that takes many iterations to get right is what separates a hobby project from one that can be used in on a live project with paying clients and real deadlines, where consequences for errors or omissions can be the difference between a job succeeding or losing money.
When it comes to real-time project execution, there is little room for error or retries; clients are watching and time is money.
In the following section we will explore the components that make up an agentic harness.
State is where the project lives: an ephemeral conversation vs. a persistent file.
Tools are what the agent can actually act on. Responding with pure text verse performing work in a project
Trusted sources is what knowledge the model pulls from (its training memory verse a carefully curated knowledge base.
Validation is what checks its work. You reading and suggesting corrections each time verse wiring audits, port matching, agent checks and routing algorithms.
Collaboration is who else can see and edit the work. A private chat in your personal account verse a shared project your team works in real-time.
State vs ephemeral
Imagine an employee shows up to the job site every morning with no memory of the work done yesterday. An ephemeral agent works this way: each session starts from scratch, and anything it learned, decided, or built is gone unless someone gets it up to speed. A stateful agent keeps a record of the work, so it knows what’s been installed, what’s been approved, and how the project is done, and it picks up where it left off.
Tools
Think of the model as a brain and the agent as a robot with that brain. The brain can think, but it can only act through the robot’s body, and what the robot can do is limited by the hands and instruments it’s been given. In a harness, those hands are tools: the brain picks one from a list of available tools and uses it to act on the world.
Trusted data
Trusted data is data that has been validated as accurate, and can be cited. For example, an agent is tasked with searching the internet for product information prior to performing a task. The agent may pull data from a dozen sources, all of which tell a different story, and some of which may be outdated or inaccurate. As a result, the agent accepts the false data and performs operations in the project based on a potentially false assumption. The solution for this is to provide the agent with an accurate picture of a devices capabilities, all of which are available through the manufacturers documentation. Such documentation can be ingested and used by the agent to perform critical tasks. A vector store database is an AI searchable database which stores the manufacture documents, allowing the agent to quickly retrieve documents find key concepts and perform work with accurate inputs.
Validation
Validation occurs prior to responding to the user, or writing to the project. For example, a simple form of validation in a wiring schematic can ensure compatible ports are wired together using compatible and consistent cable ports.
Collaboration
Technicians and engineers may require assistance when working on a project, the typical solution is to have a meeting and screen share so a plan of action can be made. The problem with this is that typically no actual work gets done, and time is wasted. With real-time workspace collaboration in the cloud, a fellow engineer can open the same project another is working in to provide guidance or even direct assistance in the tool itself, essentially increasing the workforce capacity and throughput of the work being applied.