Turn a publication archive into agent memory.

AITrailblazer AI Agent Publishing converts published research into a reusable agent workflow: archive context, TripCode identity, River memory, MongoDB-backed state, Gemini-ready synthesis, and a rendered user packet.

Opening voiceover: This demo shows a publishing agent that starts with research content, assigns a stable TripCode, reconstructs the surrounding River, remembers the session, and returns both raw JSON proof and a polished HTML packet that another agent or reader can reuse.

✓Functional running application, not a slide-only concept.

✓Google Cloud runtime surface with browser and API demo paths.

✓Gemini-ready synthesis loop with bounded evidence packets.

✓MongoDB-oriented memory model for archive, TripCode, and River state.

✓Repeatable 3-minute demo with replayable commands and UI actions.

✓Cost-aware usage ledger and protected demo-key access.

Run 3 minute demo → Open manual controls HUT-RIVER-001

What the browser demo proves

Start with a publication archive, run the deployed Cloud Run agent, inspect machine-readable JSON, then render the same response as a user-facing research packet.

ProblemResearch archives are valuable, but passive prose is hard for agents to execute.

Agent loopDiscover, invoke, verify, remember, render, and reuse the packet.

Demo proofThe same route supports manual testing and the 3-minute video sequence.

Archive briefPublication inventory becomes article objects, TripCodes, Rivers, and reusable reader prompts.

TripCode resolveHUT-RIVER-001 becomes structured River memory with MongoDB fit and execution trace.

Gemini-ready packetThe agent returns evidence boundaries, memory follow-up, usage visibility, and synthesis inputs.

Rendered outputThe latest JSON response becomes a polished HTML report for users, agents, and reviewers.