← 3 Agents


Conceptual summary: Traditional deterministic agents existed long before AI, but were rigid and difficult to scale because human language and semantic relationships required hardcoded logic.

Modern TF/UFA systems made such agents vastly more practical by providing semantic capabilities such as:

  • Semantic interpretation of human language
  • Structured constrained outputs (JSON, schemas, workflows)
  • Contextual reasoning over long token sequences
  • Task decomposition / workflow synthesis
  • Ontology alignment / semantic normalization
  • Generalization beyond explicit hardcoded rules

The core control loop itself remains ordinary deterministic software (“extAgent”), while the LLM provides semantic functionality.


3.2.1 Intro

Just like iAgent and TF work together internally, the extAgent and LLM must work together externally.

  • 1 Deterministic agents existed before AI
  • 2 TF/UFA semantic capabilities made modern agentic systems practical
  • 3 extAgent orchestrates deterministic execution
  • 4 LLM provides semantic functionality
  • 5 Model Interface Layer (MIL)


3.2.2 Diagrams

Core diagrams (my own) that explain the architecture.

  • 1 Ecosystem
  • 2 extAgent <> LLM interface
  • 3 Workflow diagram / simple
  • 4 Workflow diagram / detailed


3.2.3 AI infrastructure

Agents require external orchestration layers that augment the TF/UFA semantic engine capabilities (such as Tools, Filesystem access, RAG,MCP, APIs, and Workflow orchestration).


3.2.4 Basic demos

Very basic demos that show how deterministic agents use TF/UFA semantic capabilities.

  • 1 Basic tool
  • 3 Filesystem
  • 5 RAG
  • 6c MCP + LLM tool choice.


3.2.5 PAL demos

PAL (Palantir) demos focus on:

  • ontology systems
  • semantic workflows
  • deterministic execution
  • LLM-assisted planning
  • structured outputs
  • external orchestration


26.0518