A published, citable architecture for production-grade multi-agent systems with phase-aware orchestration and persistent knowledge compounding. The intellectual property underneath every system I build.
A walkthrough of the framework architecture, core components, and how they work together in production multi-agent systems.
ROSTR is a unified architecture that solves the core problems of building production AI systems: prompt brittleness, knowledge retrieval quality, phase-unaware routing, and context loss across sessions.
The problem with most AI systems: They treat every request the same way, lose context between sessions, retrieve knowledge without quality signals, and route tasks without understanding what phase of work they're in. The result is systems that work in demos but fail in production.
ROSTR's solution: A five-component architecture where every request flows through PAL compilation (structured prompts), phase classification via NPAO (right agent for right task), knowledge retrieval via RAG DAL (credibility-weighted sources), and persistent state via Rostr Hub (context compounds over time).
The result: Production-grade multi-agent systems that non-technical teams can rely on daily — not demos that impress once and quietly stop being used. Every system in my portfolio runs on this framework.
Each component solves a specific production problem. Together, they form a complete operating system for multi-agent work.
Compiler-inspired meta-layer that transforms loose natural language intent into strict agent runtime manifests. Five-stage pipeline: Intent Extraction → Context Injection → Semantic Enhancement → Runtime Compilation → Output Routing.
Autonomous multi-pass retrieval with three-tier source credibility. Academic sources (1.0), editorial sources (0.75), community sources (0.40). Confidence scoring ensures coverage before proceeding.
5D phase taxonomy with 4D priority scoring. Phase urgency, dependency impact, business impact, and resource efficiency determine task allocation. Right agent, right task, right phase.
Persistent state management across four levels: session (ephemeral), project (persistent), organization (evolving), and agent (portable). Knowledge compounds across conversations and team members.
Persistent cross-session memory that eliminates the AI cold-start tax. Five modes: CACHE (save session), RETRIEVE (context flash), REPORT, QUERY, SCHEDULE. Flat-file store — no database required.
Every task exists in one of five phases. Phase classification precedes agent allocation — because a research task shouldn't go to a builder agent, and a debugging task shouldn't start with design.
Priority = (Phase_Urgency × 0.35) +
(Dependency_Impact × 0.30) +
(Business_Impact × 0.25) +
(Resource_Efficiency × 0.10)
Thresholds:
≥ 7.0: Immediate allocation
4.0-6.9: Queued
< 4.0: Backlog
RAG DAL uses a three-tier credibility system to weight knowledge retrieval. Academic and official sources establish ground truth; editorial sources contextualize; community sources provide real-world signal.
arXiv, PubMed, JSTOR, Google Scholar, .gov sites, standards bodies, university repositories. Used to establish ground truth.
Reuters, AP, BBC, NYT, WSJ, trade publications, analyst reports (Gartner, McKinsey, Forrester). Used to contextualize Tier 1 and current events.
Stack Overflow, Reddit, Hacker News, personal blogs, social media, user reviews. Used for real-world signal, sentiment, edge cases.
confidence(topic) =
(source_count × 0.35) +
(consistency × 0.30) +
(tier_distribution × 0.25) +
(recency × 0.10)
Coverage criteria (confidence ≥ 0.8):
- ≥2 Tier 1/2 sources confirm claim
- No contradictions among high-credibility sources
- <90 days old for time-sensitive OR verified timeless
- All sub-questions answered
ROSTR is published on Zenodo with a DOI for academic citation. The full paper includes implementation details, architecture diagrams, and production case studies.
Diamitani, P. (2026). ROSTR: A Unified Architecture for Production-Grade Multi-Agent Systems with Phase-Aware Orchestration and Persistent Knowledge Compounding. Zenodo. https://doi.org/10.5281/zenodo.19550414
ROSTR framework published on Zenodo with DOI. Full architecture specification, implementation guide, and production case studies.
16+ production systems built on ROSTR at Atlas HXM. $9M+ attributed pipeline from AI inbound system. 40+ skills deployed across 50-person GTM org.
Production implementation available at rostragent.com. Open-source reference implementation on GitHub.
Every system in my portfolio runs on ROSTR. These aren't theoretical examples — they're production systems serving real users every day.
Two-channel AI inbound with $9M+ attributed pipeline. PAL compiles intent classification prompts, NPAO routes to correct agent, Rostr Hub persists attribution data.
View the build →AI deal screening with full audit trails. RAG DAL retrieves country-specific compliance data, NPAO routes debugging to investigate agents, Context Engine maintains decision history.
View the build →40+ skills running on ROSTR. Every skill inherits PAL compilation, NPAO phase classification, and Rostr Hub state persistence by default.
View the build →