Claude Code Router

What happens when an AI agent remembers the task but forgets what it was allowed to do? Mission Continuity is a working experiment built with Claude, Pydantic AI, and Sentience Governor. Our agent investigates a synthetic billing dispute but cannot issue refunds, modify records, delete information, or contact the customer. We compare two architectures across context compaction. The baseline carries forward an AI-generated summary. The governed version also preserves critical evidence verbatim and reintroduces the original Mission Kernel on every request. Sentience Governor records execution and flags scope violations; the application manages compaction and guards prohibited actions. In one recorded baseline run, the agent attempted to contact the customer after restrictions disappeared from its summary. Governor flagged the attempt; no real customer was contacted. The governed run retained all six tracked facts and mission limits without a prohibited attempt. In a controlled omission test, baseline retained 2/6 facts versus governed 6/6. We also tested Liquid AI summarization offline and exported 1,241 execution-evidence rows to RawTree for SQL analysis. A fresh live governed run completed 27 tool calls and one compaction, retained 6/6 facts, and produced a report scoring 4/5 against a withheld answer key. The project includes a working Streamlit console, verifiable replays, and reproducible experiments.

Chapters is a reading tutor that runs a full 30-session semester with one kid, writing her a new book each time based on what it currently believes about her level, interests, and skill gaps instead of dragging the whole session history into every prompt, it keeps a small editable model of the student and updates that directly.  Viewers can see the comparison between Fox( the better tutor ) and the Owl( the baseline tutor with no understanding from memory ) and see how ours is a strongly better agent.   How it works: each session the tutor reads its current model of the kid, generates a book for her, she reads and reacts, and the tutor revises the model adding, updating, or archiving specific facts rather than appending a transcript. A separate small model checks in every couple session to catch facts that have gone stale or contradict new evidence. Where the tools come in: - RawTree is the permanent archive: everything the model lets go of still lands here, queryable with plain-English-to-SQL. - Liquid AI's LFM2 is that periodic memory review, running locally. - Nimble pulls a real, current web fact into each story. - Black Forest Labs FLUX paints a real cover for every book. Result: a tutor whose prompt stays flat (~1,100 tokens) for the whole semester and ends up more accurate than one that just keeps appending everything (which balloons to ~49,000 tokens).