What Are the Standards for a Good AI Agent?

The value of an AI agent should be judged separately from a large language model (LLM) that simply generates answers.
That is why the key question is not, 'Which AI is smarter?' Instead, we need standards for whether the agent accurately understands the user's intent and completes the assigned work with reliable results.

Here are the 3 key criteria used to evaluate the quality of a great AI agent:

First, the criteria for success must be clear.

A good AI agent is not a system that merely responds to a request. It must satisfy the completion conditions agreed upon with the user.
It breaks a goal down into specific tasks and defines what should count as completion. Therefore, performance should be evaluated not by the fluency of its answers, but by the work completion rate, error rate, level of human intervention, processing cost, and time. This is what we call the completion condition.

For example, the goal of a customer inquiry handling agent is not simply to write a reply. It should identify the customer's intent, retrieve accurate information, solve the problem, and record the handling details. If an inquiry cannot be resolved on its own, it should transfer the case to the person in charge along with the necessary information.

In this way, an agent's success must be evaluated based on whether ‘The real work was finished exactly as I intended.’

Second, it must maintain state and direction even during long tasks.

An agent must remember the steps it has completed, the work that remains, the evidence it used, and the causes of failure.

For example, imagine an AI agent that supports a company's marketing work. It should be able to manage which stage has been completed among target setting, content creation, channel-by-channel execution, and performance analysis. If the response is lower than expected, it should analyze data by channel and creative asset, then propose a revised execution plan. If the process requires a budget change or a brand-level judgment, it should organize the rationale and options and ask the marketer to review them.
In other words, when results differ from expectations, the agent should revise the plan. When it cannot resolve something on its own, it should hand control back to a human. This is because momentary reasoning ability and long-term completion ability are different.

Third, the execution process and the basis for judgment must be verifiable.

A good AI agent is not a system that only shows the final result. It must record the information and tools it used, permission requests, failures and retries, and the results reflected in the actual work environment so that its performance can be improved.

Anthropic's guide to agent evaluation describes execution records as the full process, including outputs, tool calls, intermediate results, and interactions. It also argues that the results left in the actual environment after the task is finished should be verified separately.

Source: Anthropic, Demystifying evals for AI agents

In other words, an agent's performance should be evaluated by the process it followed and by ‘What it actually completed.’

Experience the Unexperienced

Palette believes that the competitiveness of an AI agent is determined less by the size of the model and more by the reliability, controllability, and economics of work completion. Recently, many LLMs have been racing to release new models. But an LLM is only one component of an agent.
Before choosing a new model, you must first clarify the work the agent will take on, the completion conditions, the scope of its authority, and the method for verifying its results.

Palette does not stop at showing what AI can do. We design, together, what AI will take responsibility for in your work and how it will complete that work. Go beyond asking what AI can do, and design with Palette what AI should be responsible for in your work and complete to the end.