Agent Harness Engineering Explained in 20 Mins!

Author: Pavan Belagatti · Watch on YouTube

Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.

Original language: English

Original video: Agent Harness Engineering Explained in 20 Mins! · Channel: Pavan Belagatti · 23:58 · Watch on YouTube

Topics: AI agents, Task and workflow automation, Software engineering with AI

Summary

The video explains agent harnesses as the context, tools, instructions, guardrails, and feedback surrounding an AI model, and describes harness engineering as building that environment so agents can perform software engineering tasks reliably. A tutorial demonstrates an agent using Port for service context and rules to fix a payment fee bug, with the harness limiting file access and running tests to verify the change. The video then shows Port workflows that check harness readiness before allowing deployments, including human approval for gold-rated services and blocking services that do not meet the required standard. It also demonstrates an AI agent, invocation logs, and a dashboard for querying service readiness.

Key points

An agent harness wraps around the model and manages the environment needed for long-running tasks.
The harness provides context, instructions, tools, workflows, guardrails, human approvals, error handling, and monitoring.
Harness engineering focuses on building the right system around a model rather than relying only on a capable model.
In the payments tutorial, Port supplies service ownership, the expected fee, coding rules, and allowed file scope.
The harness changes the payment fee from 10% to 2% and runs pytest to verify the edit.
Port workflows check a service’s Harness Readiness Scorecard before routing a deployment request.
A service at gold readiness can proceed to a human approval gate, while a service below gold is blocked.
The demo also shows self-healing monitoring, AI invocation records, and a dashboard for readiness and service queries.

Chapters

Start	Chapter	Summary
00:00	What an agent harness is	The video introduces the need for more than code generation and explains the harness as the environment around an LLM.
05:45	Payments app tutorial setup	The presenter introduces a Python payments app with a fee bug and describes how Port provides the relevant service context and rules.
09:22	Fixing and verifying the bug	The harness retrieves Port context, restricts the edit to app.py, fixes the fee, and runs tests to verify the result.
14:01	Harness workflows in Port	The video introduces a Port-based Harness Engineer agent, readiness scorecards, and workflows for deployment checks and self-healing.
17:12	Deployment gates and readiness	A gold-rated service proceeds to human approval, while a service with basic readiness is blocked from deployment.
20:50	Agent logs and readiness dashboard	The presenter reviews the blocked deployment’s AI invocation and demonstrates a dashboard for readiness data and service questions.
22:26	Querying service readiness	The dashboard agent checks the payment processor’s readiness rules and reports that it is ready to deploy.

Quotes

agent harness is everything that is wrapped around an LLM or the AI agent — 00:31

model is more of intelligence, and harness is everything around it — 03:38

the harness doesn’t blindly trust the generated code — 08:19

the harness controlled the file scope and verification — 12:28

AI agent itself can’t do anything. It needs to have all this environment — 20:50

Concepts and entities

Harness Engineering, agent harness, Port, Harness Readiness Scorecard, Harness Deployment gate, Harness Self Healing Monitor, pytest, Harness Engineer

Full transcript (original language)

00:00 Hey guys, so we all know that AI coding agents and AI assistants are getting really good at writing code. But here is the problem, right? Writing code is only a small part of real software engineering. An agent needs to know things like which service am I working on? Who owns it? What rules should I follow? What am I allowed to change? How do I know if my change actually works? And that is exactly where Harness Engineering comes in. And in this video, I’m going to explain everything about Harness Engineering in a very simple way

00:31 and then we’ll build a simple Harness Engineering tutorial at the end. So let’s go. So to understand what exactly is Harness Engineering, we need to understand what is an agent harness. So agent harness is everything that is wrapped around an LLM or the AI agent, right? Basically, LLM is the brain of the agent and apart from that, everything else that is around that forms the environment of that LLM becomes the agent harness. As you can read here, right? An agent harness wraps around model to manage long-running

01:02 tasks reliably. The model generates responses and the harness handles everything else. See, model is not just enough to you know, for running proper tasks, right? You need to have context, APIs, the tool calling, the the the tools, the the integration of different tools and all these things that make the agent harness for the model, right? So as you can see in the image, the analogy, I think you can easily connect it. The model is the chef and the harness is the restaurant.

01:32 It understands your request, the model understands, the chef understands the request, reasons, and creates the output. Basically, chef understands only understands the order and he will prepare prepare the dishes and that’s it. He’s creative, very powerful, and he is the main guy in the restaurant. But he doesn’t run the restaurant itself, right? So everything else, he needs ingredients, he he needs different tools to prepare the dish. So, that becomes the the harness, right? So, it provides ingredients like context, tools,

02:04 workflows, guardrails, human approvals, sub agents, error handling, and monitoring to make sure the job gets done reliably. So, So, think of this way, right? LLM is just one part of the whole ecosystem, and everything else around it, everything else, the environment around it, that is the agent harness, right? So, I hope you understood. Let’s So, so let’s move forward to understand more about harness engineering. So, what is harness engineering, right? So, think of an AI model as the brain, just like a

02:35 human brain, the model can reason, generate code, and make decisions, which is very powerful, but a brain alone is not enough. You need to engineer the environment around that brain, right? So, that environment is the harness, right? So, just like human brain, just like a human body, brain is very powerful. We can reason, we can think, we can take actions, we can do lot many things with the brain, but but to work efficiently, we need to have other body parts also, right? So, here in the

03:07 context of harness engineering, everything around an LLM or the AI agent makes the harness. So, a harness gives the agent the right context, clear instructions, access to the right tools, guardrails around what it can do, and feedback to check whether its actions actually work or worked, right? A very simple architecture looks like how it is shown in the image, right? The harness is everything we build around the model to help it operate reliably and efficiently. So, you can think of it

03:38 like model is more of intelligence, and harness is everything around it like context, instructions, tools, guardrails, feedback, right? So, this is the core idea behind harness engineering. So, in the image you can see a simple harness engineering stack. So, we need to understand that harness engineering is very important to build efficient agentic systems, okay? And as now, as AI agents take on more responsibilities across the software development life cycle, giving our agent or the LLM access to a few tools is no longer enough. We need

04:11 an agent harness, right? Think of the harness as the operational environment around an AI agent. It manage It manages inputs, configurations, tool access, execution, outputs, and feedback, giving agents the structure they need to perform real engineering work reliably and efficiently. As you can see the harness engineering stack, it all starts with the There is a user client. It can be developer, SRE, or anybody in the engineering ecosystem or in the engineering team. And then you can see the AI agent, the

04:42 LLM agent, and everything around it, right? The the tools and integrations which form the agent harness. And this agent is also supported with the agentic SDLC platform to automate engineering and developer workflows, right? Where we will see engineering proper engineering context set, standards and guardrails set, and and ownership and teams and software catalog set properly, right? And that is not enough. We need to have evaluation and observability layer also

05:14 to make sure what we are doing is tracked properly, right? So, that’s about the harness engineering stack. So, it is never about choosing the smartest model as we already know. So, it is always about building the right system around the model, and it is how you build the efficient agentic engineering systems. And that’s where our agent harness basically helps us to build such efficient systems, right? So, finally it’s the tutorial time. So, what

05:45 are we building today? For this tutorial, I wanted to keep things very simple and we have a tiny Python payments application and there is one bug intentionally put in the code. The application is currently charging a 10% payment fee, right? But the correct fee should actually be 2%. Uh so that is the bug. And here is the interesting part. We are not going to tell our AI agent directly that the correct fee is 2%, okay? Instead, that information will live inside Port, port.io, the

06:15 agentic SDLC platform that we are using in this tutorial. And you should know that Port can also be used as a context layer and that is how we are using Port here, right? Apart from the agentic SDLC platform, Port can also be used as the context layer for your engineering agentic engineering systems, right? So Port knows the engineering context about our service. It knows everything, the dependencies and all those things, like service name, the service name it knows, the payment service, and owner also it knows, the payments team, expected fee

06:46 is 2%, and coding rule preserve negative amount validation, and also there is a rule that what we can edit, what the agent can edit, and what agent can’t edit. So agent agent can actually edit the app.py to to give us a proper suggestion of what is the bug and what needs to be changed, right? And these are these are the things that our context lake knows, that Port knows, right? So here is the architecture that looks like what we’re building today.

07:17 The flow is very simple. The AI model is the brain while the agent harness controls the environment around that brain. We know that. And uh it starts with the developer making a request, for example, fix the payment fee bug, right? As you can see in the image, right? Uh he tells the agent that fix the payment fee bug. And the request first goes to the agent harness, not directly to the AI model or the AI agent. The harness gets the relevant service context from port, the context layer, and it gets the

07:48 things like the service information, ownership, dependencies, engineering rules, and what the agent is allowed to edit and not not not to do, right? So, the harness then combines the request, port context, instructions, and guardrails, and sends the task to the AI model, the AI agent, right? And then the AI model reasons about the problem and generate the required code change. And in our example, it changes app.py fixing the incorrect 10% fee to the correct 2%

08:19 value provided through port, right? Because port knows exactly what needs to be changed, and it knows the exact value, fee value, right? That is 2%, but but the incorrect one is 10 10%, which is already a bug, right? But the But now, the harness doesn’t blindly trust the generated code. It runs pytest to verify the change, right? And then the test results become feedback. If the test pass, the change is verified. If they fail, that feedback can be used for another iteration. So, the completion loop is

08:51 like developer requests a request thing, right? A developer first requests, and then it goes to the port context, and then the harness gets the request, and then AI model does, you know, asks for the code change. It provides everything, and then the tests will run, and then the feedback we will get the feedback, right? So, that’s a complete harness engineering, giving AI the right context, boundaries, tools, and feedback loop to reliably complete the engineering

09:22 tasks, and that is what we are building today, right? So, let’s see this in action. Hey guys, so here is the payments application code, as you can see, app.py. It is very simple. So, you can see that 0.10 {comma} 2. So, 0.10 should have been 0.02, right? Now, the correct answer is 2% the fee structure. Whereas, now if you calculate, it is 10% and we can also verify that using the test. And it shows that there is a

09:55 error. There is a bug and it fails. Right? And now, that’s the app.py with the bug like we said already. And this is the harness.py which acts as a controller of the whole project, right? So, instead of directly asking an LLM to fix our code, what harness.py does, it first collects the right context, right? So, it collects the right context from port.io. So, port is our context layer, right? So, let’s go to our

10:27 port.io. So, this is the port.io, the agentic SDLC platform where you can basically build and automate a lot of your developer and engineering workflows. Right? So, I already have my account. So, let me show you how the service is configured, right? So, I can go to builder and search for harness service and I can actually see what is related to this service, right? The service name is payment service. The owner is payments team. And expected fee

10:57 percentage is 2%, right? And these are the coding rules, as you can see. Payments fee must use Payments fees must use the configured fee percentage blah blah blah and all that thing. And also, what the agent can edit. The agent can actually edit only app.py. So, this is everything related to payment service. So, all the context is actually present in our board. io, that is the context layer for our application, right? For our service.

11:28 Now, let’s go back and let’s actually run harness.py to basically basically run the diagnosis and to see whether this actually works or not, right? So, let’s run We saw that there is a bug. Now, let’s run the a python harness.py. So, as you see, once I run, so it is reading service context from port and getting all the context from port. Who is the owner?

11:58 What is the expected fee? And what is the coding rule? So, now, if you see, from 0.10, it changed suddenly it changed to 0.02. So, we told the There was a coding rule we set saying that you can edit app.py. So, now, we And even the test pa- test passed, right? Giving port context plus code to the coding agent and harness approved edit to app.py and then it edited the

12:28 app.py with a proper number. And it also ran the test. And everything is here, as you can see, right? Harness result change verified, port supplied the context, the model supplied the code change, the harness controlled the file scope and verification. No production deployment capability was given to the agent. So, as you saw now on your screen how, you know,

12:59 how we actually ran the agent harness successfully, right? So, model is just a part of the whole process, but the coding rules, the context, everything was the dependencies, everything was provided by port, the the context layer, right? So, this is how this is This was very simple. I wanted to show like how model is not just like, you know, model could have just gone and edited the thing, but uh we told the agent to do what not to do,

13:30 and we gave proper context, and that’s how we we gave, you know, that was the harness that was given to the the environment that was given to the agent, and the LLM, right? So, hope this was useful, and I know this was very simple to basically help you understand how the how the harness engineering basically works, and we can actually extend this and do a lot more. And uh yeah, this is simple con- con- like, you know, concept of harness

14:01 engineering I wanted to explain and show you. Hey guys, so we saw a simple tutorial on harness engineering. And now, I want to show you another demo or uh tutorial that you can build inside Port itself. We saw what is Port. Port is an agentic SDLC platform where you can automate a lot of your dev workflows, engineering workflows, and you can also build an software agentic software factory, and so on. So, now I would like to show you how you can build this agent harness

14:33 inside Port itself, because inside Port you can create workflows, you can create AI agents. So, let me show you the agent that we have, right? I have created an AI agent. If I go to AI agents, so I have created an AI agent with the name Harness Engineer. So, what it does, there is a description, right? Domain-specific agent for harness engineering helps developers understand and improve their services’ harness readiness,

15:04 enrich missing metadata, and request govern govern deployments. Only allows deploys for services that have reached gold on the harness readiness scorecard. So, now, only this AI agent by itself can’t do anything if I run it, right? So, it needs to have the proper context and the tools. So, that context and tools is provided by port itself. How? Because this we are doing for the services. So,

15:36 now let’s go to services and see, these are all the services that we have in our engineering organization, right? You can add more services here. Let’s go, for example, prompt engineering service and I can go to scorecards. You can this different scorecards like production readiness and here is what I have created the harness readiness scorecard. This service has already reached the gold gold label, right? See, from and these are the rules of the what all it needs

16:08 to have to reach the gold. Uh so, this particular service, prompt engineering service, is already at gold, right? And now let’s go and see some other service that’s not reached gold yet, right? Let’s go to scorecards. This is also at gold. And if I go to notification service scorecards, see, this harness readiness is very basic because it is not fulfilling these these things, right? As as you can see, has a read me, freshness, a branch protection,

16:41 has a team and all those things. So, sorry, that’s for production readiness. If I click harness readiness, this is at basic again. So, these are the things it is not having yet, right? So, it is missing a lot of rules. So, it is at basic now. So, what should happen where if I want to deploy this particular service, it should get blocked. So, how it will get blocked? So, for that we have created some workflows, right? So, if you go to workflows, you can see under harness

17:12 engineering these two workflows that I have created. One is Harness Deployment gate. Fetches the service, checks its Harness Readiness Scorecard. Gold is required and only routes to approve if the service is gold. Otherwise, it gets blocked. Let’s see how this particular workflow looks like. It gets the service context and checks the service’s Harness Readiness Score. If it is at gold, then it will move that

17:43 to the for any human. There is a human gate to approve or reject it. Or you if the service is not if service’s Harness Readiness Score is not gold, it will get blocked, right? And the AI agent will explain why deployment is blocked. And we have another workflow as we saw from here under Harness Engineering, Harness Self Healing Monitor. So, it fires when a service entity is updated and a Harness critical property, for

18:14 example, tier, Slack channel, runbook, or anything gets cleared. If we remove anything from that particular service, this gets triggered automatically, right? So, this is a simple example. Now, let’s go to any of the service. And let’s go let’s pick the payment processor, right? And if I go to Scorecard, it is at the Harness Readiness Score is already at gold because it follows all the rules, right?

18:45 Now, let me try to do the like, you know, deploy this service to production, right? So, let’s deploy this. production And now I’ll execute this. If I execute this, our first workflow will run. Since it is already at gold, the the Harness Readiness Score, it should it should ask for the approval because it will go it will take this route. And it is asking me for the approval as you can see, right? Now, we can go here. We can go to

19:15 pending. It is asking me to approve. I’ll approve. And once I approve this, it will get triggered properly, and this went properly, right? So, this is the first first harness deployment gate workflow that we created. Since the service was already at gold, it went through, and there was a human gate. We approved it, and the service went through to production, right? And now, if I go to any of the other service, which is not yet at the

19:48 scorecard. Okay, this is also at gold. Let me go to checkout service. And see the scorecard. See, harness readiness score is basic because it is not, you know, meeting all these standards, right? So, now, if I go, click it, and if I try to deploy this, if I execute this, again, the same workflow will run. It will fetch the data, service data, and the dependencies.

20:20 And then, see, it took this path because the service is not yet at the gold standard, right? The harness readiness score is not yet at gold. So, it is basic. So, that’s why it is going to this particular It is taking this particular route. And this is our AI agent, by the way. So, AI agent itself can’t do anything. It needs to have all this environment, right? The the dependencies it needs to know, the all the context and the tools it needs to know to get at a particular get at

20:50 any particular decision, right? So, that is what we have created here. It’s a very simple It’s a very simple and basic basic tutorial or example, right? So, this is how exactly the workflow works. So, now, let’s go to the AI invocation page that we have. Let’s go to EI invocation to see what all ran completely, right? So, this is a minute ago and I can see we ran that workflow, right? So, you can see the related entities, the runs, the

21:21 audit log, prompt. See, the service checkout service was just blocked from deploying to production because it has not reached gold on the Harness Readiness Scorecard. Its current level is basic. Look up this service. So, it is giving all that that is the prompt from the EI agent that we used and this is the response. See the amazing response that we received, right? Harness Readiness Score is basic, blocked from gold. Right? The service is stranded at basic. It

21:53 cannot advance until every rule at each level passes in order. Here is the complete picture, right? So, this is a complete response that we received from the EI agent, right? Execution logs and this is some summary and all those things. So, you can check all this in the EI invocations. From catalog, you can go to EI invocations and check the recent runs from here, right? And yeah, there is also if you if you go to catalog, there is also a

22:26 Harness Engineering Dashboard that we have created. You can create such amazing dashboards from Port. Well, if I click here, we can go to create new dashboard. Hey, so this is my Harness Engineering Dashboard that I have created and you can see the description here what this dashboard is all about and these are the total services that we have and Harness Readiness distribution, how many services are at gold, how many are at silver and how many are at basic and it

22:56 gives us an idea and these are all the services and their tiers and here I can actually ask any of the harness engineering related uh query because this is our agent that we have created. Harness engineer agent, right? Is uh payment process ready to deploy? If I ask that, it should give me an answer, right? It will go fetch all the related um dependencies of the particular service, and it will answer.

23:28 So, it is fetching that, and uh it will give me an answer. So, you can create such amazing dashboards here, right? Yeah. So, as you can see, payment service payment processor is gold, ready to deploy. All Harness readiness rules pass, right? So, these are the rules it is passing, six by all the six rules passing, and it is at gold standard and tier one. Slack channel is team payments, and run book on-call rotation is configured, everything is done, and this is ready to deploy. So, you can ask different questions here. This is our agent that

23:58 we have created. We created it, right? So, that’s it. Go to port and create. This way also you can create the agent Harness or Harness engineering tutorial. Thank you.