# PRD-108: First Production Run

**Date:** 2026-03-21 **Branch:** ralph/prd-82b-mission-intelligence **PR:** #181 **Commit:** 3f0f8ab30

## What This Is

The first known production test of a shared semantic vector field for multi-agent LLM coordination. Real agents. Real embeddings (Qwen 2048-dim). Real Qdrant. Real mission.

## Infrastructure

* **Platform:** Automatos AI (Railway)
* **API:** automatos-ai-api (api.automatos.app)
* **Vector DB:** Qdrant (qdrant-production-c691.up.railway.app)
* **Embeddings:** qwen/qwen3-embedding-8b via OpenRouter (2048 dimensions)
* **Backend:** SHARED\_CONTEXT\_BACKEND=vector\_field
* **Baseline available:** SHARED\_CONTEXT\_BACKEND=redis (for A/B comparison)

## Mission Prompt

```
Research the current state of AI agent memory systems. I need a comprehensive
analysis covering:

1. What memory architectures exist today (RAG, shared context, message passing,
   blackboard systems)
2. What are the key limitations of current approaches — specifically around
   information loss between agents
3. What companies are building multi-agent platforms and how do they handle
   inter-agent communication
4. What does the academic research say about shared memory for AI agents
   (check arxiv papers from 2025-2026)
5. Write a final briefing document synthesizing all findings with specific
   recommendations for our platform

This is for an investor meeting. Every finding matters — don't lose anything
between agents.
```

## Why This Prompt

This mission is designed to stress-test every PRD-108 mechanism:

* **Multiple agents** researching different domains in parallel
* **Cross-agent dependency** — the writer agent needs findings from ALL research agents
* **The telephone game scenario** — without the field, the writer only sees what the analyst forwarded
* **Real semantic queries** — agents querying by meaning, not keywords
* **Temporal relevance** — findings from different research phases have different freshness
* **Resonance scoring** — the most semantically relevant findings surface first

## What To Watch

1. **Qdrant logs:** `field_` collection creation, point upserts, query\_points calls
2. **API logs:** `[PRD-108] Created field`, `[PRD-108] Injected task output`
3. **Agent tools:** `platform_field_query` and `platform_field_inject` calls in agent execution logs
4. **Final output:** Does the writer's briefing document reference findings from ALL research agents, not just the ones the analyst explicitly mentioned?

## Evidence To Capture After Run

* [ ] Qdrant logs showing field lifecycle
* [ ] API logs showing pattern injection and queries
* [ ] Mission output (all task results)
* [ ] Field pattern count at end of mission
* [ ] Which agent findings appear in the final briefing

## Prior Test Evidence (local, same day)

Before this production run, all local tests passed:

* 33/33 proof suite (algorithms + mechanics)
* 13/13 multi-scenario (5 domains, VF wins all 5)
* 57/57 unit tests (adapter)
* 16/16 stress tests (scale + performance)
* A/B result: Vector field 62% avg vs message passing 29% avg across 5 scenarios

## The Claim

This is a novel combination of known primitives — resonance scoring, temporal decay, Hebbian reinforcement, mission-scoped fields — applied to multi-agent LLM coordination. The implementation is real, tested, and now running in production.

This is not "first ever shared memory." This is the first production deployment of a mission-scoped shared semantic vector field with resonance-based retrieval for multi-agent coordination.

Built by Gavin Kavanagh and Claude (Anthropic).