Hello Reader,

AWS released something this week that is going to confuse a lot of people. It is related to the Gen-AI Word of the Week: Harness. In this newsletter, we are going to go over what is Agent Harness and how this newly released Agent Core Harness is related to it or differs from it. And more importantly, how much should you learn about it for your interviews and real-world projects?

What a harness actually is

First let's understand what is an AI agent.

Most people focus on the LLM when they talk about AI agents. The LLM gets all the attention. But the LLM is not what makes an agent work. The code around the LLM is what makes it work.

That code controls which tools get called and when. It manages short-term memory during a session and extracts important information into long-term memory. It handles the system prompt, what gets passed to the LLM and in what format. It manages input and output from the LLM, tools, and memory. And it controls the execution loop, think, act, observe, repeat, until the task is complete. The whole things (diagram above) is an AI Agent.

Harness

This code is the harness. It is what ties the LLM, memory, tools, and associated infrastructure together into a functioning agent. Change the harness and the agent behaves differently even if the LLM stays exactly the same. This is why Cursor, Claude Code, and Kline produce different outputs on the same model. because the harness is different.

AI Agent = LLM + Harness tying together all the components

Option of Building Harness

Till now this is how you build harness - write your own harness using a framework like Strands, LangChain, or LangGraph. And deploy it on AgentCore runtime so AWS manages the infrastructure while you manage the code. This way you don't have to worry about scaling the underlying infrastructure, managing memory, implementing observability etc. Amazon AgentCore provides all that for you out of the box.

Newly released AgentCore Harness adds a second option. You configure the harness through a UI, no coding needed. In this mode you select the model from the bedrock or an external provider. You just point to the tools from the AgentCore gateway. You connect memory from AgentCore memory and then you set execution limits including maximum iterations, timeout, and token caps.

AWS writes the harness code under the hood and runs it. You do not need to know how to code.

The flexibility versus simplicity trade-off

You get simplicity in exchange for flexibility of controlling the components.

This is not a new pattern. AWS has been making this trade-off available across its entire product line for years.

EC2 gives you 800+ instance types, full control over memory, networking, storage, and compute. Lambda manages all of that for you and you just run code. EKS on EC2 gives you full Kubernetes control. EKS Auto Mode manages the node configuration for you. The pattern is always the same. More control on one end, faster movement on the other.

AgentCore Harness sits at the simplicity end of the agentic spectrum. Strands and equivalent frameworks sit at the flexibility end. Neither is better or worse. The right choice depends on the use case, the team, and the performance requirements.

What this means for your career

This is the question that actually matters and the answer depends entirely on the role you are going for.

If you are going for a GenAI Solutions Architect, Forward Deployed Engineer, or AI Engineer role, you need to know how to code the agent. Telling an interviewer you used the drag and drop UI to build an agent is not sufficient. You need Python. You need to understand tool definitions, memory management, system prompt construction, and execution loop control. You need to have built something and be able to explain every decision in it.

If you are going for a General Solutions Architect role, or a management position dealing with GenAI, or a go-to-market (GTM) or account executive role, you need to understand how to create an agent and evaluate the trade-offs between managed and self-written harnesses. You do not need to write the harness yourself. You can use AgentCore Harness or AI-assisted coding tools like Cursor or Claude Code to code the harness.

In both case you need to understand the concepts and not the tools. Understand what is harness, components of AI Agent, pros-cons of using open source harness vs cloud native managed harness etc.

Keep learning and keep rocking 🚀,

Raj

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