← 3.2 Agents + AI


Just like the LLM internal agent and TF must work together, so must the extAgent and LLM.

  • 1 Agents existed long before AI
  • 2 Agentic AI (LLMs designed for agents)
  • 3 AI agents (agents that use AI assistants to make decisions)
  • 4 Model interface layer (MIL)

External agent (left) and the LLM (right)
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1 Agents existed long before AI

GPT: “Your entire architecture is basically (1) Deterministic agent systems existed before LLMs (but were expensive and rigid) ….”

It took a decade for AI-assist to be added to Palantir agents (SW). Before AI Palantir agents used ontology (their own mini-dictionary) to limit the human language that could be used in their systems. And it worked. It required “sanitzing” all inputs, but it could process vast amounts of data, looking for that needle in the haystack that might signal another secret 9/11 style terrorist attack.


2 Agentic AI (LLMs designed for agents)

GPT: “…and (2) TF/UFA semantic capabilities made those systems vastly more practical and flexible. That is actually a very strong and coherent thesis.”

TF/UFA semantic capabilities made such systems vastly more practical and flexible by enabling:

  • semantic interpretation
  • structured outputs
  • ontology alignment
  • planning
  • contextual reasoning
  • generalization
  • explanation generation

This is one of the core reasons modern agentic AI became practical.

The following are just a few examples of how AI is a very helpful assistant for agentic apps:

  • Interpret/sanitize messy human language text
  • Create human language explanations
  • Planning (breaking up complex tasks into granular tasks that the agent can process reliably)
  • Input and output (of “messy” human language representations)
  • Rule injection (by AI into the main loop)

These capabilities make agentic AI apps

  • vastly more flexible and capable
  • at the cost of unpredictabilty

For many apps this is an excellent tradeoff.

About planning and other “intelligent” AI functionality

For background on VL, storylines, etc, discussed below, see The gist of LLMs.

AI is more about pattern matching than language. The TF generates from “messy” human language a vector language (VL) representation of the input “storyline” (I don’t prefer term “vector” for the 12288 FP numbers for each GPT-3 token; to me they are just scalar (numeric) indicators of detected semantic (meaning) features). The VL for the final token is pattern matched against all tokens in the vocabulary. The closest matching token for that pattern is selected as the next token.

Although based solely on patterns detected in massive amounts of training data, this functionality can do much more. It can be used for planning planning (taking complex planning input prompts and splitting up into smaller atomic parts that can be more easily be verified and executed by the agent), JSON specification of LLM output, etc.


3 AI agents (agents that use AI assistants to make decisions)

AI’s arrival vastly enhanced Palantir system performance because their machines could now “communicate” freely with messy chaotic human language input. But Palantir’s systems, like all AI-ic agents systems, are assisted by AI (not controlled by AI). The LLM is simply a “helpful assistant”. LLMs control nothing, push no buttons. But AI hype usually talks about “agentic AI”, not “AI agents”. Agentic AI that might one day take over the world (Skynet style; see my substack post #73 Understanding Palantir Maven / Why AI will never become Skynet). That hype generated a lot of publicity (and investment).

The following diagram is a good example. “What Is Agentic AI? Agentic AI is an innovative advancement in artificial intelligence (AI), characterized by its ability to independently make decisions and implement goal-oriented actions on behalf of users or systems.” (link)

An example of the LLM-centric agentic AI universe
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4 Model interface layer (MIL)

The LLM internal Agent and TF are customized to work together to maximize the reliability and accuracy of agentic AI functionality.

But this functionality will invariably vary to some degree between all LLMs. Therefore the AI agentic may need to “tune” its prompts and interpretations of responses. To fix this problem, Palantir created a “Model Interface Layer” (MIL) between the LLM and AI agent.


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