Apollo Agents
Complete steerability, unparalleled control
Transformer-based LLMsdon't make good agents
Traditional language models rely on transformer architectures, which excel at pattern recognition and language generation. They are particularly effective for a variety of tasks such as text generation, translation, and summarization.
However — when it comes to agentic use cases, situations where the model needs to perform actions, make decisions, or interact with tools — transformer-based LLMs face significant challenges.
that’s where apollo comes in
Transparency
A transparent, white-box model
Controllability
An advanced instruction environment
Predictability
A structured reasoning process
Tool-use
100% successful tool-use
Fine-tuning
Fine-tuning via human feedback
Better suited for agents
The neuro-symbolic approach bridges the gap between the flexibility of neural networks and the precision of symbolic logic. By merging neural networks with symbolic reasoning, Apollo offers several key advantages.
Symbolic components reduce the need for extensive datasets. By leveraging existing knowledge bases and rules, Apollo performs effectively even with limited training data.
A higher level of understanding & precision
At the heart of Apollo’s capabilities lies the Structured Interaction State. This state is a symbolic, parameterized representation of each interaction, created by collecting and organizing sensory data.
By converting unstructured inputs into a structured format, Apollo achieves a higher level of understanding and precision in its responses.
a new level of safety & accuracy
Combining generative and rule-based approaches minimizes the risks typically associated with AI outputs.
Adheres to predefined rules to avoid generating inappropriate or harmful content.
