Soviet Chess: A Multi-Agent Framework for AI-Assisted Software Development with Reinforcement Learning

1 · Introduction / Introducción

EN Large Language Models (LLMs) have transformed software development through tools like GitHub Copilot, Cursor, and Claude Code. However, a single AI agent handling architecture, implementation, testing, and deployment simultaneously produces characteristic failure patterns:

ES Los Modelos de Lenguaje (LLMs) han transformado el desarrollo de software con herramientas como GitHub Copilot, Cursor y Claude Code. Sin embargo, un solo agente IA manejando arquitectura, implementación, testing y deployment produce patrones de fallo característicos:

1. Context Loss / Pérdida de contexto: When subtasks are delegated to sub-agents, each loses visibility of the whole system. In our observation, this caused 138 compilation errors from incorrect import guessing during a module split. / Cuando las subtareas se delegan a sub-agentes, cada uno pierde visibilidad del sistema completo.
2. Error Accumulation / Acumulación de errores: Writing multiple files before compiling allows errors to compound. One wrong import in file 1 cascades to 138 errors across 5 files. / Escribir múltiples archivos antes de compilar permite que los errores se multipliquen.
3. Role Confusion / Confusión de roles: A monolithic agent optimizes for the last instruction rather than holistic quality. Qian et al. (2023) showed that role separation reduces hallucination by 66%. / Un agente monolítico optimiza para la última instrucción en vez de la calidad global.

EN We address these with a structured multi-agent system inspired by Minsky's Society of Mind (1986), chess organizational metaphor, and blackboard architecture (Hayes-Roth, 1985).

2 · Architecture / Arquitectura

2.1 Three-Layer Model / Modelo de tres capas

  ┌──────────────────────────────────────────────────┐
  │           LAYER 3: PRODUCTION                     │
  │  Agents that OPERATE the product (24/7)           │
  │  Content strategy, publishing, moderation         │
  │  ♚ ♛ ♜ ♝ ♞ ♟                                     │
  ├──────────────────────────────────────────────────┤
  │           LAYER 2: DEVELOPMENT                    │
  │  Agents that BUILD the product (per-session)      │
  │  Architecture, coding, testing, deployment        │
  │  ♚ ♛ ♜ ♝ ♞ ♟                                     │
  ├──────────────────────────────────────────────────┤
  │           LAYER 1: INFRASTRUCTURE                 │
  │  Shared tools: MCP, Git, Compiler, DB, Tests      │
  │  Blackboard (shared memory between agents)        │
  └──────────────────────────────────────────────────┘

Figure 1: Three-layer architecture. Each layer uses the same 6 chess-piece roles for different domains. / Figura 1: Arquitectura de tres capas.

2.2 Agent Roles / Roles de agentes

Table 1: Agent roles across layers. Minimal authority principle enforced. / Tabla 1: Roles de agentes por capa.

3 · Blackboard Communication / Comunicación por Pizarra

EN Agents communicate exclusively through a shared blackboard (Hayes-Roth, 1985), never directly. This ensures full traceability and prevents context loss.

ES Los agentes se comunican exclusivamente a través de una pizarra compartida, nunca directamente. Esto asegura trazabilidad completa y previene pérdida de contexto.

              ┌─────┐
              │ Rey │──── writes task + contract ────┐
              └─────┘                                │
                                                     ▼
                                            ┌─────────────────┐
   ┌─────┐                                 │   BLACKBOARD     │
   │Torre│──── writes file ────────────────▶│                 │
   └─────┘                                 │  context/        │
   ┌─────┐                                 │  signals/        │
   │Cabal│──── reads file, writes test ───▶│  artifacts/      │
   └─────┘                                 │  reviews/        │
   ┌─────┐                                 │                 │
   │Reina│──── reads all, writes review ──▶│  ALL agents     │
   └─────┘                                 │  can READ       │
                                            └─────────────────┘

Figure 2: Blackboard communication pattern. / Figura 2: Patrón de comunicación por pizarra.

3.1 Import Map Protocol / Protocolo de mapa de imports

EN Before any module split, Torre writes a dependency map to the blackboard. This prevents the import-guessing problem that caused 138 errors in our initial approach.

  BEFORE splitting a 3,675-line file:

  Step 1: Torre reads entire file
  Step 2: Torre extracts → import_map.json:
          {
            "external_imports": ["sumud_db::repo::travelers", ...],
            "public_functions": ["handle_message", ...],
            "cross_references": {"handle_command": ["handle_admin_command", ...]}
          }
  Step 3: Torre writes import_map to blackboard
  Step 4: For EACH submodule:
          a) Read import_map from blackboard
          b) Write ONE file
          c) cargo check  ← COMPILE IMMEDIATELY
          d) Fix any errors (typically 0-2)
          e) Next file

Figure 3: Import Map Protocol prevents cascading compilation errors. / Figura 3: El protocolo de mapa de imports previene errores en cascada.

3.2 Task Contracts / Contratos de tareas

EN Every task includes machine-verifiable pre/postconditions (Meyer, 1992). The critical postcondition is cargo check = success after every file write.

  task: "Split channels.rs into submodules"
  preconditions:
    - cargo check: success
    - file channels.rs: exists, 3438 lines
  postconditions:
    - cargo check: success         ← MANDATORY
    - cargo test: all pass         ← MANDATORY
    - channels.rs: deleted
    - channels/: exists, 5 files
    - max file: ≤1500 lines
    - all original functions: preserved

4 · Generate-Check-Fix Loop / Ciclo Generar-Verificar-Corregir

EN The core innovation: treating the compiler as a fast feedback oracle (2-5 seconds per check) and invoking it after every single file operation.

  ┌──────────────┐
  │  Write file  │◄──────────────┐
  └──────┬───────┘               │
         ▼                       │
  ┌──────────────┐        ┌──────┴──────┐
  │ cargo check  │───Yes──│  Fix errors │
  │ (2-5 sec)    │ Errors?│  (1-2 fixes)│
  └──────┬───────┘        └─────────────┘
         │ No errors
         ▼
  ┌──────────────┐
  │  Next file   │
  └──────────────┘

  Cost of checking early: 5 seconds
  Cost of 138 accumulated errors: 30+ minutes

Figure 4: Generate-Check-Fix loop. Adapted from TDD's Red-Green-Refactor (Beck, 2003). / Figura 4: Ciclo Generar-Verificar-Corregir.

5 · Reinforcement Learning Module / Módulo de Aprendizaje Reforzado

5.1 Formulation / Formulación

EN We model agent strategy selection as a Contextual Bandit problem (Langford & Zhang, 2007). The state is the codebase condition, actions are strategy choices, and rewards are composite efficiency metrics.

R = w_tokens · TokenEff + w_errors · ErrorEff + w_quality · QualityDelta + w_tests · TestDelta + w_tools · ToolEff + w_time · TimeEff + w_rework · ReworkPen

Table 2: Reward function components. Weights are the policy parameters updated by the analyzer. / Tabla 2: Componentes de la función de recompensa.

5.2 Daily Analysis Cycle / Ciclo de análisis diario

  ┌─────────────┐      ┌─────────────┐      ┌─────────────┐
  │  Sessions   │─────▶│  Analyzer   │─────▶│  Updated    │
  │  (JSON logs)│      │  (daily     │      │  Strategy   │
  │             │      │   cron job) │      │  Weights    │
  └─────────────┘      └──────┬──────┘      └──────┬──────┘
                              │                     │
                              ▼                     ▼
                       ┌─────────────┐      ┌─────────────┐
                       │   Report    │      │ Next Session │
                       │  (markdown) │      │ (uses new   │
                       │             │      │  weights)    │
                       └─────────────┘      └─────────────┘

Figure 5: Offline RL feedback loop. / Figura 5: Ciclo de retroalimentación RL offline.

5.3 Policy Update / Actualización de política

EN We use Exponential Moving Average (EMA) updates, inspired by Thompson Sampling (1933). For boolean strategies, we compare average reward when the strategy is active vs inactive. For continuous parameters, we move toward the value that produced the highest reward.

w_t+1(s) = w_t(s) × (1 - α) + target(s) × α    where α = 0.1

ES Usamos actualizaciones de Media Móvil Exponencial (EMA). Para estrategias booleanas, comparamos la recompensa promedio cuando la estrategia está activa vs inactiva. Para parámetros continuos, nos movemos hacia el valor que produjo la recompensa más alta.

6 · Validation / Validación

6.1 Experimental Setup / Configuración experimental

EN Validated on the Sumud Project: a 51,297-line Rust/TypeScript codebase with PostgreSQL, Redis, Telegram bot, Instagram integration, and Astro frontend. Over 5 development sessions.

Table 3: Before/after comparison on Sumud codebase. / Tabla 3: Comparación antes/después en codebase Sumud.

6.2 Key Observations / Observaciones clave

EN The Import Map Protocol eliminated cascading errors. In Session 1, splitting channels.rs (3,438 lines) without an import map produced 0 errors — because Torre compiled after each file. Splitting traveler_bot.rs (3,675 lines) using subagents WITHOUT the import map produced 138 errors. After implementing the protocol in Session 2, the same operation produced 4 errors.

ES El Protocolo de Mapa de Imports eliminó errores en cascada. En la Sesión 1, dividir channels.rs (3.438 líneas) con compilación por archivo produjo 0 errores. Dividir traveler_bot.rs (3.675 líneas) usando sub-agentes SIN mapa de imports produjo 138 errores. Tras implementar el protocolo, la misma operación produjo 4 errores.

EN The compiler as oracle is the highest-value feedback signal. At 2-5 seconds per check, it provides near-instant verification. The RL analyzer's error weight (0.25) is the highest, reflecting its empirical importance.

7 · Related Work / Trabajo relacionado

ChatDev (Qian et al., 2023): Role-based chat chains for software development. Soviet Chess differs in using a blackboard instead of chat chains, and adding an RL feedback loop.

MetaGPT (Hong et al., 2023): SOP-encoded agent roles. We share the SOP concept but extend it with machine-verifiable contracts and stack-specific profiles.

AutoGen (Wu et al., 2023): Microsoft's multi-agent conversation framework. Soviet Chess is specialized for software development with compilation feedback, while AutoGen is general-purpose.

SWE-Agent (Yang et al., 2024): Single-agent code editing with search/edit tools. Soviet Chess adds role specialization and the RL optimization layer.

Generative Agents (Park et al., 2023): Agent simulation with memory and reflection. We adopt the memory concept (blackboard) but in a production engineering context rather than social simulation.

8 · Conclusion / Conclusión

EN Soviet Chess demonstrates that structured multi-agent collaboration, combined with compilation-as-oracle feedback and offline RL strategy tuning, dramatically reduces the failure modes of AI-assisted software development. The three key contributions are:

1. Import Map Protocol — eliminates cascading errors in module refactoring (-98.6% compilation errors)
2. Generate-Check-Fix loop — compiler invoked after every file, not at batch end
3. Offline RL analyzer — daily strategy weight optimization based on session metrics

ES Soviet Chess demuestra que la colaboración multi-agente estructurada, combinada con compilación como oráculo de retroalimentación y ajuste de estrategia por RL offline, reduce dramáticamente los modos de fallo del desarrollo de software asistido por IA. Las tres contribuciones clave son:

1. Protocolo de Mapa de Imports — elimina errores en cascada en refactoring de módulos
2. Ciclo Generar-Verificar-Corregir — compilador invocado después de cada archivo
3. Analizador RL offline — optimización diaria de pesos de estrategia

EN The framework is stack-agnostic (validated on Rust, TypeScript, and Astro), open-source, and designed for incremental adoption — teams can start with just the compile-check loop and progressively adopt role separation, contracts, and RL tuning.

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Soviet Chess Framework v2.0 — David Pineda with Claude Opus 4.6 — April 2026. Licensed under CC BY-SA 4.0.