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).