design · machine-learning · agents

Machine learning agentAtlas

Atlas is the KBVE machine learning agent project and a compact reference for the core ideas around autonomous ML systems: how agents act, how models are trained, how neural networks are layered, and how the supporting pipeline fits together. The project also ships as a container image for a local launch.

Concepts

Launch locally

Atlas ships as a container image — launch it with docker run -d -p 3000:3000 kbve/atlas.

AtlasProject

kbve/atlasImage

ML agentsTopic

Autonomous

Agents make decisions, take actions, and adapt from feedback within defined operating boundaries.

Training

Models learn from data as an algorithm adjusts parameters to reduce error and improve predictions.

Layered

Deep networks stack layers of nodes so each stage can transform signals into more useful representations.

Agent

An entity that observes an environment and selects actions toward a goal or reward.

Systems

The full workflow around data collection, preprocessing, training, validation, deployment, and monitoring.

Concept glossary

Building blocks of ML systems

Overview

Atlas is a KBVE project focused on machine learning agents. This page keeps the current project notes in one place and defines the terms that show up around autonomous agents, training loops, layered networks, and ML systems.

Autonomous

In the context of machine learning agents, autonomous means that an agent can make decisions, take actions, or complete tasks without step-by-step human instruction. The agent still works inside the boundaries set by its training, available data, algorithms, tools, and runtime environment.

Learning and adapting: An autonomous agent can use feedback from its environment or prior runs to improve future behavior.
Decision-making: The agent evaluates available context and chooses an action based on patterns, policies, rules, or learned strategies.
Human oversight: Autonomy does not remove human responsibility. Deployed agents still need review, updates, guardrails, and intervention paths.
Self-correction: More advanced agents can detect errors or inefficient behavior and adjust their strategy.
Goal-oriented behavior: Autonomy is usually scoped around a task, objective, or reward signal.
Environment loop: The agent observes, processes information, acts, and receives feedback from the environment.

Autonomy is a spectrum. Some agents operate only under strict conditions, while others have broader access to tools, memory, or external systems.

Training

Training is the process where a machine learning model learns from data so it can make predictions or decisions on new inputs. Instead of writing every rule by hand, the training process adjusts the model until its outputs better match the expected results.

Data: Training starts with a dataset made of inputs and, when supervised learning is used, the correct outputs.
Model architecture: The architecture is chosen for the task, such as linear regression for simple trends or a neural network for image, text, or sequence work.
Learning algorithm: The algorithm updates the model’s internal parameters after comparing predictions against expected outputs.
Loss function: The loss function measures the gap between the model’s prediction and the target result. Training tries to minimize that error.
Iteration: Training usually runs across many passes so the model can keep reducing error until progress slows or stops.
Validation: A separate validation dataset helps detect overfitting, where a model memorizes training data but performs poorly on new examples.

After training, the model is evaluated on test data it has not seen before. That final evaluation gives a better read on whether the model can generalize beyond the training set.

Layered

A layer is a group of nodes that operate together at a specific depth inside a neural network. Each layer receives an input, transforms it, and passes the result forward.

Deep learning models use multiple layers to build richer representations over time. Convolutional neural networks, recurrent neural networks, transformers, and other architectures all rely on this layered composition, even when the shape of the layers differs.

Agent

An agent is an entity that observes an environment, decides what to do next, and acts on that environment. In robotics, the observation might come from sensors and the action might move hardware. In software, the observation can be messages, files, API responses, or application state, while the action can be a tool call, generated output, or workflow step.

The agent usually works toward a goal or reward. It improves by learning which actions lead to better outcomes, instead of depending only on fixed instructions for every possible case.

Systems

A machine learning system is the full set of data, models, algorithms, infrastructure, and operational processes needed to solve a task with data-driven learning.

Data collection: Gather relevant source data, such as text, images, sensor readings, events, logs, or user interactions.
Data preprocessing: Clean, normalize, label, filter, or transform the data into a format suitable for training.
Model selection: Choose the model family or algorithm that fits the task, constraints, and available data.
Training: Let the model learn patterns by adjusting its parameters against a loss function.
Validation: Tune the model and catch overfitting before final evaluation.
Testing: Evaluate the trained model on data it has not seen before.
Deployment: Move the model into a real environment where it can serve predictions, decisions, or agent behavior.
Maintenance and monitoring: Track performance after deployment and retrain or fine-tune when data, requirements, or behavior changes.

What is an autonomous machine learning agent?

In machine learning, autonomous means an agent can make decisions, take actions, and perform tasks without explicit human instruction. It relies on its training, data inputs, and algorithms, learning and adapting from its environment and self-correcting over time, though autonomy is a spectrum rather than absolute.

What does training mean in machine learning?

Training is the process where a model learns from a dataset of inputs and correct outputs. A learning algorithm iteratively adjusts the model's parameters to minimize a loss function, with validation on a separate dataset guarding against overfitting until the model converges.

How do you run the Atlas image?

You can launch the Atlas image with docker run -d -p 3000:3000 kbve/atlas.