Agent Training Data: A Guide to LLM-Based Agent Training

Agents based on Big Language Models (LLMs) are now changing the way businesses, researchers, and developers interact with data and make decisions. The success of these agents depends on the model architecture, quality, diversity, and management of the training data.

This guide provides an overview of the sources, generation methods, curation strategies, and practices for data preparation. This allows LLM-based agents to operate safely and effectively in real-world environments.

Quick Take

Quality input data ensures reliable results.
To connect datasets to measurable tasks, operational roles need to be defined.
Blended strategies help strike a balance between speed, security, and scale.
Methods improve efficiency, reduce evaluation time, and control costs.
Human feedback and explanatory information minimize production risk.

Agent training data

Training LLM agents depends on the quality and provenance of the training data for agents. Agents learn to perceive context, plan actions, interact with the environment, and make decisions. Therefore, the data for them must cover real-world scenarios, synthetic interactions, and be carefully curated.

Category	Subtype	Short Description	Why It Matters for Agents
Data Sources	Real data	User logs, business documents, system telemetry	Shapes realistic behavior in live environments
	Public datasets	Open text, code, and RL datasets	Provide general knowledge and reasoning patterns
	Expert-generated data	Scenarios and decisions from domain experts	Ensures correct logic and reduces errors
Data Generation	Synthetic data	Data generated by LLMs or simulators	Scales training and covers edge cases
	Self-play/Multi-agent	Agents interact and learn from each other	Teaches strategies rather than fixed responses
	Reinforcement learning data	Environment interaction via reward signals	Optimizes decision-making and action sequences
Data Curation	Cleaning and filtering	Removing noise, duplicates, unsafe content	Improves reliability and safety
	Annotation	Labeling goals, actions, and outcomes	Enables causal and structured reasoning
	Debiasing	Detecting and mitigating data biases	Produces fair and stable agent behavior
	Versioning and lineage	Tracking data origin and changes	Ensures reproducibility and enterprise control

Data quality and representativeness

For LLM agents, quality data is data that has logic and thought patterns that they internalize.

Quality measurements:

Accuracy. The data reflects real facts, events, or correct decisions.
Completeness. There are no gaps in scenarios, roles, or contexts.
Timeliness. The data is relevant to current conditions, rules, and policies.
Consistency. There are no logical contradictions between sources.
Noise vs. Signal. Useful information dominates over random or irrelevant samples.

Data representativeness

Representativeness means that the data accurately reflect the real distribution of the environment in which the agent operates. Let's consider the main aspects of representativeness:

Scenario coverage. There are typical and rare cases in the data.
Balance of roles and contexts. Different types of users, languages, and interaction styles.
Distribution of actions and decisions. Not only "successful" or "ideal" cases.
Correspondence to the environment. The data corresponds to the real conditions of use.

So, quality determines how correctly the agent thinks, representative of how widely it is ready to act.

Labeling and feedback

Labeling is the process of assigning a structured meaning to data that an agent can use to learn not only responses, but also behaviors and decisions. Unlike classical models, agents need multi-level annotations:

Goal — what the agent needs to achieve
State — the context in which the decision is made
Action — a specific step or tool call
Outcome — the consequence of the action
Quality of the decision — correct/acceptable/incorrect
Rationale — why this decision is correct or not

Labeling is performed by domain experts from various fields, trained annotators, or other models (such as auto-labeling with verification).

Feedback

Feedback is a signal that the agent receives after an action and uses it to correct its behavior. By collecting preference data from human evaluators, such as ranking outputs or providing feedback. Agents can learn which actions or responses are preferred, further improving performance and alignment.

Types of feedback:

Type	Description	Use / Pros / Cons
Human Feedback	Response rating, ranking multiple options, comments with explanations	Used in reinforcement learning from human feedback (RLHF) and critical agent training; highly contextual, captures intent
Automatic Feedback	Metrics, rule checks, simulation results	Scalable; drawback: may miss intent or nuanced errors
Hybrid Feedback	Combination of automatic evaluation and selective human review	Most common in enterprise systems; balances scalability and accuracy

Choosing a training approach

There are different approaches to training agents and LLMs that define how the model acquires knowledge and forms behavior. Each method has its own advantages, limitations, and optimal application scenarios. The choice of approach depends on data availability, task complexity, and the requirements for security and accuracy.

Learning Approach	What It Is	Advantages	Disadvantages
Supervised Learning	Model learns from labeled data	Fast training, high accuracy on known data	Requires many high-quality labels; does not teach strategies
Self-Supervised Learning	Model generates signals from the data itself without external labels	Can leverage large datasets; learns general patterns	Slow to train; may learn noise or undesired patterns
Imitation Learning	Agents learn by observing agent demonstrations or expert behavior	Learns complex action sequences; quickly approximates expert level	Limited to available examples; may not learn optimal strategies
Reinforcement Learning (RL)	Agent learns through rewards and penalties by interacting with the environment	Teaches optimal policies; adaptable to new situations	Slow training; requires careful reward design; risk of undesired behavior

Platforms and tools for training agents

Agent platforms can be categorized based on functionality and intended use:

Type of Platform	What It Does	Why It’s Needed
Data Management Platforms	Store, version, and curate large datasets	Ensure data quality, representativeness, and reproducibility of training
Annotation/Labeling Tools	Allow experts or annotators to create structured labels for data	Provide clear signals for agent learning and support causal reasoning
Model Training Systems	Enable distributed training, model optimization, and experiment management	Allow efficient training of large models and agents on diverse data types
Simulators/RL Environments	Create controlled environments for agent interaction	Used for reward-based learning and strategy testing
Monitoring & Evaluation Tools	Measure agent performance, track errors, hallucinations, and biases	Ensure safe and stable deployment of agents
Integration & API Services	Allow agents to interact with real-world systems and tools	Enable practical deployment of agents in business workflows

Keymakr specializes in creating high-quality training data for artificial intelligence models, including computer vision and other machine learning applications. The company collects, annotates, verifies, and classifies data, combining human experience with automated validation. This approach ensures high-quality data. Its proprietary

Keylabs data annotation and management platform provides tools for machine labeling, project workflow management, team collaboration, and support for multiple data formats. These tools help organizations prepare consistent and scalable datasets needed to train LLMs and intelligent agents.

Testing and validation

Testing and validation are important stages in the LLM agent lifecycle. The primary goal of these processes is to verify that the agent performs its functions correctly and safely before deploying it for interaction with real users.

The first stage of testing takes place in a sandbox environment. This is a controlled environment that simulates real conditions, but without risk to users. In the sandbox, the agent interacts with synthetic data, simulators or pre-created scenarios. This allows you to evaluate its behavior, reaction to edge-case situations, the correctness of its decisions, and compliance with established rules.

Such testing allows you to detect errors, hallucinations, biases, or unwanted behavior patterns at an early stage.

In the second stage, the agent interacts with real users, a process known as phased rollout or pilot testing. They start with a limited group or test scenarios. It enables you to evaluate the agent in real-world conditions, considering a range of requests, user patterns, and unpredictable contexts.

It is essential to collect user feedback, success metrics, and behavioral data to refine the agent and optimize its actions before a large-scale launch.

The combination of sandboxing and limited user testing allows you to achieve a balance between security and reliability. Without this approach, the agent may exhibit unwanted behavior in critical situations or provide incorrect answers, resulting in a loss of user trust and damage to the business's reputation.

Problems and practical solutions

When training and implementing LLM agents, recurring problems arise that can compromise the system's security for users. It is essential not only to identify these problems but also to develop practical solutions to address them.

Problem	Description	Practical Solution
Low-quality data	Data contains errors, noise, or incorrect examples	Use filtering, expert annotation, and human-in-the-loop verification
Data bias	Incorrect or biased representation of groups, scenarios, or roles	Apply debiasing, dataset balancing, and representativeness checks
Model hallucinations	Agent generates false information or inaccurate answers	Use RLHF, additional verification sources, and confidence-based response limits
Poor reward design	Incorrect reward signals lead to undesired behavior	Design clear reward functions, apply penalties for errors, decompose goals into sub-tasks
Limited generalization	Agent fails to adapt to new scenarios	Train on diverse datasets, use self-play, and synthetic data augmentation
Deployment risks	Agents may make mistakes in real-world environments	Use gradual rollout, sandbox testing, and collect user feedback

FAQ

What sources of supervised and synthetic input data should be used?

Should real, labeled data and synthetically generated scenarios or simulations be used?

How to ensure the quality and representativeness of collections?

Quality and representativeness are ensured by carefully annotating, cleaning data, balancing scenarios, and verifying coverage of all relevant cases.

When is simulation learning better than reinforcement learning approaches?

Simulation learning is better when high-quality demonstrations of expert behavior are available, and strategies need to be quickly reproduced without lengthy trial-and-error.

What are the best practices for annotation and human-in-the-loop feedback?

It is best practice to combine expert labeling with automated validation, regularly assess quality, and provide structured feedback.

What testing and validation regime should precede deployment?

Before deployment, the agent should be sandbox-tested and then validated with a limited group of real users.

What are the common problems and pragmatic strategies to mitigate them?

Common problems include low-quality data, bias, model hallucinations, weak generalization, and deployment risks. Pragmatic strategies include data filtering and annotation, debiasing, RLHF, diverse training sets, sandbox testing, and gradual rollout.