What is an AI Agent Harness?

An AI agent harness is a software environment that surrounds a large language model (LLM) to provide it with the necessary tools and context to complete multi-step tasks. The harness enables the LLM to operate as a system that executes real-world tasks.

Inside the harness, agents typically run in a loop. At the heart of this loop is the ReAct paradigm, which synergizes reasoning and acting in language models. The loop consists of three stages: reason, act, and observe.

In the reason stage, the LLM reads all available context, including the task, relevant memory, and previous results, then decides what action to take next. In the act stage, the harness carries out the chosen action by executing a tool, running code in a sandbox, calling an API, or writing to storage. Finally, in the observe stage, the harness captures the result of the action and feeds it back to the LLM as new context.

The model then uses this result to decide what to do next, and the loop continues until the task is complete.

To illustrate the concept, consider a scenario where a file named "sales and vendor commission.txt" is present in the local system. The goal is to perform a complex calculation on the data within this file. The final prompt instructs the agent to read the file, extract the column with the header "Sales Person Name," calculate its word count, and determine the vendor commission based on the word count.

The commission is calculated as 15% of the "Sales" column if the word count is 20, or 10% if the word count is also 20. Additionally, the current UTC time should be added to the report for each row. To accomplish this, the agent requires specific tools.

The tools that the agent will have access to include:

1. calculator: This tool evaluates basic arithmetic expressions involving addition, subtraction, multiplication, division, exponentiation, and modulo operations. It is used for performing mathematical calculations instead of doing the calculations manually.

2. get_current_time: This tool retrieves the current date and time, optionally offset by a UTC offset in hours. By default, it provides the time in UTC (offset = 0).

3. word_count: This tool counts the number of words and characters in a piece of text. It takes the text as input and returns the word count and character count.

4. read_local_file: This tool reads the contents of a text file from the local working directory. The file path is restricted to the sandbox directory for safety, and the filename is provided as input.