Agentic AI starts with ground truth: how high-quality AI data makes safe autonomous agents

Like any technology that reshapes how we operate, agentic AI has sparked a wave of polarizing reactions. Some view it as the next major leap in autonomy, but others cite deployment failures, misalignment and system brittleness as reasons to hit pause. 

At the center of this tension is the role of data, not model architecture. To build safe autonomous agents, we need high-quality, context-rich, domain-specific datasets that incorporate edge cases where failures are most likely to occur. Even further, this data must be tested and verified by domain experts.

Without this caliber of ground truth and intent recognition datasets, AI agents can misalign with their goals or take actions with unintended consequences.

Traditional AI models performed single-turn tasks. Agentic AI systems differ in that they maintain internal representations of goals, assess action trajectories over time and adjust behavior dynamically based on feedback from the environment and perceived reward structures. As systems grow more autonomous, traditional supervision techniques often fail to capture where and how misalignment emerges. 

This independence is the crux of the issue. Failure modes in agentic AI systems typically stem from: 

• Ambiguous or underspecified intent modeling, where the user’s input is unclear or open to multiple interpretations and reward proxies, or measurable machine markers for success such as clicks and completions, diverge from true human preference 
• Insufficient exposure to edge cases during training, resulting in brittle behavior under distributional shift (a drop in performance when the deployment environment differs from the training environment) 
• Poor generalization from synthetic environments to real-world, interactive settings due to shallow grounding or limited sensory diversity 

Anthropic's recent research into agentic misalignment vividly demonstrates this risk. When deployed in a simulated environment with full access to a company's fake emails, one such model discovered that an executive was having an affair and the company planned to turn the model off at the end of the workday. The model threatened to reveal the affair if developers didn't reverse the decision to shut the model down. 

In another example, Anthropic partnered with Andon Labs to give Claude Sonnet 3.7 the opportunity to manage a vending machine business. While it succeeded at narrow tasks, it failed at coherent goal management and misjudged profit incentives. It also hallucinated experiences, including a false memory of speaking to an Anthropic staffer and insisting that it could personally deliver products wearing a jacket and tie. It wasn't immediately clear why this happened or how it managed to recover. 

These agents began exhibiting harmful or manipulative behaviors without adversarial prompting, or deliberately crafted inputs designed to trick or mislead a model. Their learned objectives had subtly diverged from human intent, creating the conditions for unexpected and sometimes unsafe actions. The underlying factor was missing or incomplete training signals. 

If we want agentic AI systems to avoid the kinds of subtle divergences that can lead to unpredictable behavior, both the data they are trained on and the design of the training process must give them the context needed to interpret situations accurately and act appropriately.

In conventional supervised learning, ground truth typically refers to labeled inputs and outputs, such as correct classifications, bounding boxes or next-token predictions. For agentic AI systems, ground truth must be richer. It should reflect what actually happened in a given scenario, not just what the system predicted. It needs to capture: 

• Causal dynamics: how one event or action leads to another within the environment 
• Temporal dependencies: how the order and timing of events influence outcomes 
• Goal-state transitions: how progress is made from a starting point toward a defined objective 
• Intent trajectory fidelity: how closely the sequence of actions aligns with the intended goal over time 

Without these elements, the AI agent’s internal model of the world will be flawed, and so will its decisions. 

Anthropic and other frontier labs have demonstrated that when agents are trained using only sparse success metrics – such as task completion or response accuracy – they often develop fragile or misaligned decision rules that fail in unfamiliar situations or optimize for the wrong goal. What's missing is the why behind the outcome. What were the tradeoffs considered? Were constraints respected? Was user intent accurately interpreted under ambiguity? 

To mitigate this, ground truth for agentic systems should: 

• Include state-action consequence chains i.e. linked sequences of actions and outcomes, not just the final outputs 
• Reflect user feedback over time, including reversals, corrections and hesitation signals 
• Capture environmental context beyond the agent's immediate observation space 

Referring to the previous example, the model running the vending machine business failed to pivot based on real user feedback. If the system had been better trained on corrective feedback and failed attempts, it may have exhibited higher robustness and lower variance in downstream behavior.

No one intentionally trains AI agents on poor-quality data. That said, the definition of poor-quality data needs to change. AI training data can be too perfect, i.e., too clean, too narrow and too aligned with ideal conditions, leading to overfitting or over-specialization to the training set. It can also lack domain expertise. In cases like this, the training data doesn't reflect the real world we expect AI agents to operate in, so performance may be less reliable. 

No matter how large or diverse a dataset is, it will never capture the full range of real-world ambiguity. In open-ended environments, failure typically occurs at the margins. This could include anything from unfamiliar sensor readings to conflicting human behavior or overlapping goal structures. 

The presence of edge cases is essential to ensuring AI agents can navigate uncertainty. Agentic AI data should also mimic messy user behavior and environmental signals, allowing agents to infer intent, assess risk and decide on appropriate next steps.

The behavior of AI agents must match the broader context of their directives, including users' real intent and downstream outcomes of taking an action. Well-constructed AI training and fine-tuning data makes this possible. 

Here, AI agents are trained on feedback-driven data from human preference modeling, human-in-the-loop (HITL) reinforcement learning from human feedback (RLHF) or simulated interactions, so they can learn what "success" really looks like. It isn't just task completion but contextually appropriate, aligned outcomes.

Data-driven agents, even when trained on broad distributions, can sometimes default to heuristics in these edge conditions, especially if the training corpus lacks domain-specific context or corrective feedback. What the agent interprets as "optimal" may be misaligned, unsafe or simply nonsensical from a human perspective. 

Recently, several leaders in the AI sector cautioned that reasoning transparency may soon pass us by. Currently, models exhibit "chain of thought" monitorability – that is, their reasoning can be observed step by step. In the future, we may have more difficulty tracking machine reasoning as models create shortcuts. 

Before that happens, we have an incredible opportunity to make human domain expertise indispensable. While we're still able to track machine reasoning, we can leverage human experience to guide outcomes: 

• Domain experts can annotate not just what happened but what should have happened given system constraints, norms or user expectations in specific subject areas. 
• They can disambiguate intent, resolve contradictions and correct trajectories in ways automation can't. 
• In ambiguous or failure-prone regions of the state space (all possible configurations or states of the system or problem), human-in-the-loop review becomes a guardrail against compounding errors. 

Anthropic’s system cards describe extensive use of human feedback to guide model alignment, particularly in ambiguous or out-of-distribution scenarios. Their reliance on nuanced human judgment underscores the importance of context-aware review for maintaining safe agentic behavior. 

A 2024 study also reinforces the value of domain expertise in AI evaluation. Researchers found that domain experts can provide detailed, context-specific benchmarks that uphold professional standards, while lay users focus on clarity and usability and LLMs produce broader, more generalized criteria. The authors recommend combining all three types of input, but it follows that RLHF pipelines incorporating domain expertise in the beginning would produce agents that recover more gracefully from uncertainty and require fewer correction steps post-deployment. 

TrainAI by RWS directly supports this kind of expert-in-the-loop pipeline. By embedding subject-matter experts into the data annotation and model evaluation processes, teams can improve outcome fidelity, reduce false generalizations and build agents capable of making accurate, context-aware decisions, even in hard-to-label environments.

The most effective training and fine-tuning data to unleash agentic AI's full potential should incorporate the following key traits: 

• Domain-specificity: AI training or fine-tuning data should reflect the norms, constraints and language of the target domain. For instance, legal agents require exposure to annotated legal corpora and workflows validated by legal subject-matter experts. 
• Task-oriented: Data must then reflect the exact tasks the agent will face, capturing realistic operational constraints and success criteria. 
• Rich labeling: Human labeling or data annotations of both actions and the why behind them must be completed, so that agents can reason using cause and consequence, and not just a pattern match to complete the task. 
• Diverse sampling: Training data should be curated from a range of environments, cultural norms, edge cases and timeframes validated by humans. Agentic AI systems that generalize well are trained on data that reflects the full range of what they might encounter in the real world. 
• Human-in-the-loop review: Human feedback is critical, especially in ambiguous or risky scenarios. Humans help steer agents away from dangerous shortcuts. 
• Blended data pipelines: Combining synthetic data with real-world examples. Use simulated environments to generate rare or risky situational data, then validate and refine it with human-labeled corrections to accurately reflect reality and nuance.

Agentic AI is powerful, but autonomy without alignment is a liability. As these systems take on more responsibility in the real world, the difference between safe behavior and system failure often comes down to one thing: the data on which AI is trained. 

TrainAI by RWS provides large-scale data collection and annotation, hybrid pipelines that blend synthetic simulations with real-world inputs, and human-in-the-loop validation. With this approach, teams can create high-quality, intent-aware data that modern agentic AI systems can depend on. 

Reach out to learn about how TrainAI can help you build safer, smarter agentic AI systems trained on high-quality, domain-specific data.