2023… Prompt Engineering
2024… Context Engineering
2026… Agent Harness Engineering
Since ChatGPT was released in 2022, AI tools have been easily and freely accessible to every other human being. Now that anyone can easily build AI agents through a perfectly written prompt, what does an AI Engineer do except writing the so-called better prompts?
AI Engineering is no longer about only writing code, but to build a system, design an environment, specify intent, and build feedback loops for the agent.
The new buzzword in the tech industry is the Agent Harness, and how important it is even more than the model used. But how much truth is there? Is it really as necessary as they say it is? Turns out, yes, it is.
What exactly is Agent Harness?
Agent harness is everything about the agent except for the model(LLM) itself. Think of LLM as the brain and the harness as the body of a human. The brain itself is quite powerful but can do nothing without a body.
Models are stateless and non-deterministic. With a good harness, one can build a great agentic system even with a cheap model(like GPT-3.5).
So is an agent harness a tool?
Nope. An agent harness is like the system environment built around the model. Like the system design of an agent! Agent Harness can also be defined as the control layer between a model and the real world.
Agent = Model + Harness
Another great way to understand is this- a Harness. We use harnesses for our pets so they do not run around and remain in command. Similarly, we use an agent harness so the model does not wander around and cost us millions of tokens. We are literally tying the model to a stable environment.
But why not just a better prompt?
While building an agent, what if the agent doesn’t work as it is expected to? We just write better system prompts, right? Not really. A good agent should have its system restructured by adding more and better tools, guardrails and context management.
Harness engineering is treating each failure as a system problem to fix, and not a better prompt to retry.
Why the sudden need Harnesses?
Agent harness is not something we came up with recently. It was always there. But the developers did not understand its importance. For months, they have been waiting for the next release of a better and more powerful model, while the real strength lies in better harnessing an existing model.
A great model can do so many things, but it’s like a simple chatbot. For it to get anything done other than just providing answers, it needs its harness. E.g., Claude Code not only generates the new code but also writes it in the respective files. Coding tools like Codex, Cursor, Copilot, Cline etc are great examples of harnesses. The model underneath the platform can be changed, however, the real work happens with the harness.
Harness is all about reliability- to make sure that agents do exactly what we want them to do irrespective of the model. We are already paying quite a lot for the LLM tokens, so it is important that the agents utilize their full potential.
In short, harnessing became necessary as AI moved from providing only prompt-response answers to an actual agent that gets things done. As we explore the components of harnesses further, it makes sense how each component works to provide a better experience for the users.
The below diagram by the team at Langchain provides a perfect and easily understandable architecture of agent harnessing.
The Anatomy of a Modern Agent Harness:
1. Large Language Model
The core brain used to run the agent. In ChatGPT you can set it as GPT-4o or GPT-3.5 as per your preference and your richness in terms of API keys.
There has always been a lot of pressure on deciding which LLM to use, because that does handle the greatness of an agent. However, the main purpose of Agent Harness is to take the pressure off the model and provide better results even with cheaper(or crappy) models.
2. Prompts
Another instrumental part of the agent. The better the prompt, the better the execution. It is so true and makes a huge impact. The models in AI companies have 100s of lines of prompt meticulously written to make sure that the LLM understands precisely what the user wants it to do.
A part of agent harness involves including better prompts. Though, the prompt should only contain information about the operations to execute. The entire agent harness cannot be defined through a single prompt where all the tools execution are included as part of the prompt. It should only contain the steps to be taken.
3. Context management window
Based on the prompt received, the harness performs context engineering to decide what to include, what to compress and what to remove.
There are different methods like context isolation (keeping different subtasks separate so they do not confuse with one another), context reduction (dropping irrelevant data to avoid context rot), and context retrieval (injecting fresh data such as documentation or search results). As the size of the context window is limited, it is important that the context engineering is done appropriately and the agent performs well throughout the entire lifecycle.
4. Tools
At the heart of the harness, are the tools that the model can use to connect with the external system. The most common and useful tools are web scraping(for gathering data from websites), API calls, code execution in sandbox, file read/write tool and code interpreter.
5. Memory and State Management
The harness has to maintain memory beyond a context window of a single task. This includes- working context (the immediate prompt given to the model ); session state (a durable log of what’s been done in the current task); and long-term memory (a knowledge base or vector store that persists across tasks or time). With a well performed memory and state management, the agent can utilize only the effective data from the memory and avoid feeding the model with unnecessary data.
6. Verification and Guardrails
The agent verification process ensures that the task to be performed has been executed correctly before treating the work as complete.
The guardrails make sure to take actions within certain rules and not accidentally delete the user’s entire file system.
Benefits of Harnessing:
The most important advantage of harnessing is that the AI systems become model-agnostic. As we discussed earlier, the harnessing helps the agent perform better even with cheaper and older versions of models.
Another benefit of harnessing is that it reduces the token cost by providing the model the right tools and management which can get the best results through the model’s utmost capacity.
The Future: Is this a temporary trend?
Now one might think that Agent Harnessing is a temporary concept which will be easily taken over the years when better models are available that take care of it. And it is somewhat true. The models are always improving, and the developers are always making ways to make it easier for the next generation of developers to do minimum work.
However, harnessing does not shrink or vanish, it moves. Harness engineering is like scaffolding which is always necessary while building agents. As the models get bigger and better, the harnessing moves up. A harness is a system, not a config file you set up once. It is always there.
Thank you for reading. Hope you enjoyed it. If you want to get a deep-dive of Agent Harness Engineering, do check out these References:
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