Diving into the realm of agentic AI articles feels like discovering a hidden recipe book for AI’s future – of course these recipes won’t be cakes and soups; they will consist of how to create AI that can act and accomplish things through reasoning and planning by performing their operations in the complex world. As an editor of a website that is always searching for new angles I found these dense, complicated academic documents to be full of incredible data/information that goes beyond their intended purposes. Each agentic AI paper has whispers of what/how agentic AI agents process information and how they learn through trial and error, which is undoubtedly valuable to anyone developing the datasets that are used to train agentic AI systems. So let’s discuss how we can infiltrate this archive of agentic AI papers to create more effective/smarter datasets which do not only teach the agentic AI to identify images of cats but also help the agentic AI develop a multi-step plan to strategically organize a digital photo library.
In reviewing numerous agentic AI studies, one noticeable takeaway is the significance of both context and state. In traditional datasets, we typically only see one static sample: one question, one image, or one statement. An agent’s actions all connect to create a flow of changing environment via its actions, as opposed to creating individual samples that are independent from one another. Each of the studies contained a researcher who presumed that they would need to define the environment/state/observation space of their research. If we want to improve on the design of a dataset for, let’s say, a customer service bot; we cannot continue to think in terms of isolated Q&A’s. Instead, we must create datasets that mirror what an agent would experience – storyboards of an agent interacting with the environment over four or five interactions, along with either the user giving more specific frustration to the bot, or the user re-iterating their frustration after the bot provides its info to the user. This stateful and sequential data reflects the processes of change as defined by many agent-Based AIs where an agent learns from earlier inputs, and continues to learn by updating that awareness through each additional message of a conversation.
A hidden treasure within agentic AI research lies within its examination of failures and paths to recovery. While researchers typically emphasize their innovative design and great successes, this generally comes as an afterthought with the true value being the rationale behind the failures of the agent. These agentic AI papers show a negative blueprint of where and how the agent can fail thereby indicating which areas in our dataset may have a lacking or missing quality. Therefore, if numerous other agents from agentic AI literature identify that their agents will become ‘stuck’ in a loop when an available tool is not accessible, that indicates a great need for our new task-oriented dataset for a code generating assistant to contain more than just successful examples of code generation. Our data will require far more examples of graceful degradation such as: “The API you requested is down, here is what alternative could be recommended,” and/or “Your request lacks clarity, suggest these three questions to clarify.” By mining the failure discussions in agentic AI papers, we can proactively inject these critical recovery and reasoning skills into our data, making the resulting AI more robust and human-aware.
The current trend in agentic AI is to focus on the ability of agents to connect with and use tools on the Internet via APIs, and this represents a significant shift in what agents will ultimately represent as knowledge. An agent will not be an all-knowing entity; rather, it will be a knowledge user. Effectively shifting the goal of dataset design from storing “complete knowledge” to storing “instruction sets for accessing and using knowledge” means that we will need to create datasets that extend beyond simple input/output pairs. This means that we need to create data that teaches orchestration. The following is a sample of an orchestration dataset for a travel agent with input “Plan a family trip to Paris on a budget for a weekend” and output as an action log of how to achieve this. Step 1: Find out what the weather will be like in Paris via an API call to the Weather API. Step 2: Find a family-friendly hotel via a budget filter in the database of budget hotels. Step 3: Find child-friendly events happening in Paris during that weekend via an event listing service. Step 4: Create a cohesive email from this information. This structure is action-oriented, drawn directly from agentic AI research. The purpose of this structure is to assist agents in achieving their goals by allowing them to decompose their objectives into smaller and executable tasks; this is the essence of agentic behavior.
Moreover, agentic AI papers offer an unparalleled example of what we should be evaluating. Very rarely does this evaluation utilize a solitary accuracy score, rather researchers will assess agents based upon completion rates, efficiency (total number of steps), robustness to perturbations, and safety. Therefore, it is necessary that datasets include various real-world attributes which will aid in the evaluation of agents. Since the evaluation of agents does not possess a singular answer key and agents perform differently within each of the aforementioned categories of evaluation, it will be required that for each task there should exist a spectrum of acceptable outcomes and different levels of criteria that are regarded as “i.e.” efficient along with available pitfalls or edge cases. For example, an email filtering agent’s dataset would need to provide not only “spam” and “non-spam” responses, but also test scenarios that will help evaluate the agents ability to separate out potential phishing emails that appear to be from possible emergency situations versus how well the agent follows rules that users have created which may conflict with each other. The way we develop our datasets will depend on how well we are able to create multi-dimensional evaluations based on the highly involved benchmarks contained in agentic AI research papers, as they will allow us to measure our AI’s ability to perform a specific task rather than simply matching a given pattern.
To understand agentic AI papers, one must consider the underlying philosophy of how we see AI. In contrast to traditionally seeing AI as a mathematician that performs a statistical analysis of the world through pattern matching, we need to look at AI through the lens of a digital actor just beginning its life and having some goals and means with which to achieve those goals. This shift will provide the foundation for creating our datasets. We will be asking different types of questions: For example, does this data point provide a framework for developing strategic thinking? Does this data sequence provide an example of moving past an unsuccessful attempt? Does this example show an ability to synthesize data from multiple “hypothetical” sources? The exploratory and playful approach found in many of these research studies should inspire us to take a more creative approach to how we design data to generate a more complete view of AI capabilities. For instance, if we use the guiding principles found in agentic AI papers, we can expand our opportunity to develop datasets that can be used for more dynamic and sequential purposes as opposed to strictly evaluating knowledge-based competency through rigid and narrow benchmarks. The next evolution of useful AI support systems will come from improved model architectures that combine collective researcher-created learning under the open-access philosophy with superior, smarter, and more autonomous datasets being developed by current researchers.