Open Source Palantir for AI Agents

Most agent stacks can tell you what happened.

Prompts, tool calls, tokens, latency, traces, even the retrieved chunks behind a given answer, all inspectable.

But six months later, when someone asks the harder question “why did the agent make this decision?”, the answer is a reconstruction exercise.

You search logs, replay prompts, inspect vector-store hits, and hope the underlying data has not changed since.

That is a bad architecture for an AI system making consequential decisions.

This is the problem Semantica is trying to solve, and the interesting part is the data model.

Semantica turns agent context, i.e. facts, relationships, decisions, provenance, rules and causal links into a graph you can query later.

A decision becomes a first-class object.

That changes what you can ask:

• What facts were available when this decision was made?
• Which earlier decision caused or influenced it?
• Where did those facts come from, and have they changed since?
• Have we made a similar decision before?
• What downstream decisions depended on this one?
• Did it pass a deterministic…