The last generation of large language models learned to be excellent at a fairly narrow task: given some text, predict what comes next. That objective, paired with enormous datasets and reinforcement learning from human feedback, produced remarkably capable chatbots. But a chatbot that answers questions in a single turn is a fundamentally different product from an agent that plans a multi-step task, calls external tools, checks its own work, and adjusts course when something goes wrong.
Agentic AI systems that autonomously plan, act, use tools, and pursue goals across multiple steps is quickly becoming the dominant frame for where AI product development is headed. But there's a problem underneath the hype: the AI training data that made great chatbots is not the training data that makes great agents. Static question-answer pairs and web-scraped text can teach a model to sound knowledgeable. They cannot teach it to reliably decide when to call an API, how to recover from a failed tool call, or when to stop and ask for clarification instead of guessing.
Building agentic AI well requires rethinking what training data even means. Here's what actually changes, and why it matters for anyone building or evaluating these systems.
Most pretraining data books, articles, forum posts, code repositories captures the outcome of thinking, not the process. An article states a conclusion; it rarely narrates the false starts, the reconsiderations, or the moment the author realized they needed to check a fact before proceeing. Traditional instruction-tuning datasets improve on this somewhat by pairing prompts with high-quality responses, but they're still fundamentally single-turn: one input, one output.
Agentic tasks don't work that way. An agent booking a trip, debugging a codebase, or resolving a customer ticket has to:
None of this behavior is well represented in the data that trained earlier generations of language models. A model that has never seen examples of recovering from a failed API call has no real basis for doing it gracefully when it happens in production and in agentic workflows, something failing is not the exception, it's the norm.
Agents need to learn not just that tools exist, but when to use them, how to select the right one among several options, how to format calls correctly, and how to interpret often-messy outputs. Tool-use annotation involves labeling examples of an agent's interaction with an API or function: was the right tool chosen for this step, were the parameters correct, was the output correctly interpreted, and did the agent take the appropriate next action based on that output. This is a fundamentally different annotation task from labeling sentiment or topic it requires annotators who understand both the tools being used and the downstream consequences of getting a call wrong.
Rather than training a model only on a final answer, reasoning-trace data captures the intermediate steps: the plan, the sub-goals, the checks, and the corrections along the way. This matters enormously for agents because much of what separates a good agent from a bad one isn't the final output it's whether the reasoning that got there was sound, verifiable, and repeatable. High-quality reasoning traces let a model learn how to think through a multi-step problem, not just what a plausible-looking answer looks like at the end. Verifying these traces is genuinely hard: it requires annotators to evaluate not just whether a conclusion is right, but whether each intermediate step logically supports the next one.
.Nearly every dataset built for earlier-generation models skewed toward "successful" examples a good answer to a good question. Agentic systems need the opposite emphasis, too: examples of things going wrong. A tool call that times out. An API that returns an error message the model has never seen. A plan that turns out to be based on a wrong assumption halfway through execution. Training data that includes these failure modes, along with examples of graceful recovery, is essential, because production agents encounter exactly these situations constantly, and a model that has only ever seen clean success paths will handle them poorly.
Where a chatbot dataset might consist of isolated prompt-response pairs, agentic datasets need full trajectories a complete sequence of the agent's actions, tool calls, observations, and decisions across an entire task, sometimes spanning dozens of steps. Evaluating and labeling these trajectories requires judging the coherence of the whole sequence, not just the quality of any single step in isolation. An agent might make a locally reasonable decision at step four that turns out to derail the entire task by step nine and that kind of long-horizon error is invisible if annotators only ever review isolated turns.
RLHF for chatbots typically asks human raters to compare two candidate responses and pick the better one. RLHF for agents needs to go further: raters increasingly need to evaluate entire action sequences, weighing not just whether the final outcome was correct, but whether the agent took efficient, safe, and appropriately cautious steps to get there. Did the agent ask for clarification when it should have? Did it take an irreversible action, like sending an email or making a purchase, without appropriate confirmation? Preference data for agents has to capture these judgments, which are qualitatively different from "which response reads better."
Because agents act in the world calling real or simulated APIs, modifying files, executing code there's an opportunity, and a requirement, that chatbot training data didn't have: verifying whether an action actually achieved its intended outcome. Did the code actually run without errors? Did the calendar event actually get created with the correct details? This kind of outcome verification, sometimes automatable and sometimes requiring human review, becomes a critical form of ground truth for agentic systems in a way that's less central to purely conversational models.
Labeling reasoning traces, tool-use decisions, and multi-step trajectories is a fundamentally more demanding task than labeling sentiment, topic categories, or single-turn response quality. It requires annotators who can:
This is closely tied to inter-annotator agreement and rigorous quality processes broadly, but the stakes are higher here because the judgments are more complex and the failure modes are subtler. A labeling error on a single sentiment classification affects one data point. A labeling error on whether an agent's multi-step plan was sound can teach a model the wrong lesson about an entire class of reasoning, propagating across every future task that resembles it.
Reinforcement learning from human feedback was originally built around a relatively simple structure: show a person two possible responses to the same prompt, ask which is better, and use that signal to shape the model's future outputs. This works well when the unit of evaluation is a single, self-contained response.
Agentic RLHF has to evaluate something more complex: a sequence of decisions unfolding over time, where the "right" choice at any given step often depends on what happened several steps earlier, and where the ultimate measure of success is whether the task was actually accomplished, safely and efficiently, not just whether any individual step looked reasonable.
This shift changes what human feedback needs to capture. Raters need clear guidelines not just on response quality, but on process quality: efficiency, appropriate caution around irreversible actions, correct tool selection, and graceful error handling. Building this kind of feedback loop requires closer collaboration between the people designing the agent's task environment and the people evaluating its behavior, because the definition of "good behavior" is often specific to the task domain in a way that generic response-quality judgments never had to be.
For organizations building or fine-tuning agentic AI, a few practical implications follow directly from this shift.
Start collecting trajectories, not just Q&A pairs. If your evaluation and training data still looks like isolated prompt-response pairs, you're not capturing the information an agent actually needs to learn from. Full task trajectories, including tool calls and intermediate reasoning, need to become the default unit of data collection.
Invest in domain-qualified annotators for reasoning verification. Judging whether a multi-step plan or tool-use decision was correct requires real domain expertise, not just general labeling capacity. This is a shift from breadth of annotator workforce toward depth of annotator qualification.
Deliberately collect failure cases. Don't just log successful task completions. Systematically capture and label failure modes and recovery attempts, since this is precisely the data agents need most and have the least of by default.
Redesign preference-collection workflows for agent behavior. Simple pairwise response comparisons aren't sufficient. Preference and feedback collection needs to account for process-level judgments: efficiency, caution, and appropriate escalation to a human.
Treat verification as part of the data pipeline, not an afterthought. Where possible, build automated verification of task outc
omes into the data collection process itself, so ground truth about whether an action actually succeeded isn't left to guesswork or self-reported success.
Agentic AI represents a genuine shift in what these systems are asked to do, and training data has to shift with it. Static text and single-turn instruction data can produce a model that sounds capable. Only richer, process-aware data — tool-use annotation, verified reasoning traces, deliberately collected failure and recovery examples, full multi-step trajectories, and RLHF redesigned around behavior rather than isolated responses — can produce a model that actually acts capably, reliably, and safely across the kinds of extended, real-world tasks agentic systems are increasingly being asked to handle.
Organizations that recognize this shift early, and invest in the harder, more specialized data infrastructure it requires, will have a meaningful head start over those still treating agent training as a variation on chatbot fine-tuning.
Building an AI agent? It needs data your chatbot never did. Globik AI helps you capture the tool-use annotation, reasoning traces, and RLHF signals agentic systems actually learn from. Talk to our team
Chatbot training data is mostly single-turn: one prompt, one response. Agentic AI needs to learn multi-step planning, tool use, error recovery, and long-horizon reasoning, none of which is well represented in static, single-turn datasets.
t's the process of labeling whether an AI agent selected the correct tool or API for a given step, used the right parameters, and correctly interpreted the tool's output before deciding on its next action.
Reasoning traces capture the intermediate steps an agent takes to reach a conclusion, not just the final answer. They matter because they let a model learn a sound process for multi-step problem solving, rather than just producing plausible-looking final outputs.
Traditional RLHF compares two single responses and asks which is better. RLHF for agents has to evaluate full action sequences over time, judging efficiency, safety, and appropriate caution around irreversible actions, not just the quality of a single output.
Because production agents constantly encounter tool errors, unexpected outputs, and flawed initial plans. If training data only includes clean success cases, the model has no basis for handling failures gracefully when they occur in the real world.
Annotators need domain expertise sufficient to judge whether multi-step plans, tool calls, and reasoning chains are actually correct, not just plausible. This is a more specialized, higher-skill task than typical single-turn response labeling.
The same principles apply, but the stakes are higher: judging a multi-step trajectory or reasoning trace is more complex than labeling a single response, so consistent, well-calibrated annotator judgment matters even more to avoid teaching a model unsound reasoning patterns.
