Agent
Composition of AI Capabilities
An Agent is not a separate pillar. It is a combination layer that integrates existing AI components into a single system.
At amsafis, Agents typically combine:
- Expert Systems + LLM, or
- Machine Learning + LLM
The role of the Agent is to coordinate these components, not to replace them. Each part keeps its function:
- Expert Systems provide structured, rule-based reasoning
- Machine Learning models provide predictive outputs
- LLMs provide interpretation and generation capabilities
The result is a system that can process inputs, consult multiple sources, and return a coherent and structured response.
Agents are designed per use case. There is no fixed architecture:
- some Agents use LLMs only for interpretation,
- others rely on deeper LLM-driven reasoning,
- and some incorporate additional layers as needed.
For example, an Agent may include a semantic phenotype normalization module based on vector similarity search. A working example is available in the Agent sandbox, which provides a human-readable representation of an interaction designed for machine-to-machine communication. This interface does not correspond to the human-facing system, but instead reflects how the Agent would interact with underlying services, as described in the symbolic cognitive service architecture.
This illustrates how specific capabilities can be added without redefining the overall structure.
Integrated interaction flow
An Agent connects multiple components into a single interaction.
From the user perspective, this appears as a unified system. Internally, it involves several steps:
- interpreting the input,
- structuring the request,
- consulting knowledge or models,
- and generating the final response.
These steps are not exposed as a workflow in the interface, but they reflect the coordination between different AI components.
The Agent does not make decisions independently. Instead, it organizes and aligns outputs from:
- deterministic logic (Expert Systems),
- statistical inference (Machine Learning),
- and contextual reasoning (LLMs).
This approach enables:
- traceability through structured components,
- flexibility through modular design,
- and adaptability to different domains.
The Agent therefore represents the practical integration of the three pillars.