Research-Stack/0-Core-Formalism/lean/Semantics/Semantics/AgenticOrchestration.lean

/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Research Stack Team

AgenticOrchestration.lean — Multi-Agent Coordination for Research Automation

This module re-exports agentic orchestration components from split modules:
- Hardware-native agent structures (AgenticHardware.lean)
- Core agent types, states, and tasks (AgenticCore.lean)
- Orchestration field computation (AgenticOrchestrationField.lean)
- Task assignment and orchestration algorithm (AgenticTaskAssignment.lean)
- Orchestration correctness theorems (AgenticTheorems.lean)

Split from AgenticOrchestration.lean per swarm suggestion (USER AUTHORIZED).

Agent Types:
1. SearchAgent — Literature discovery (wraps ScholarOrchestrator)
2. ExtractAgent — Concept extraction from papers
3. FormalizeAgent — Lean 4 code generation
4. ValidateAgent — Empirical benchmarking
5. SynthesizeAgent — Report compilation
6. BuilderAgent — Builder (Architect): ADD clock, proposes forward progress, builds state (manifold_reg)
7. WardenAgent — Warden: SUBTRACT clock, reverses to check, validates proofs (stark_trace)
8. JudgeAgent — Judge (HeatSink): PAUSE clock, holds state, adjudicates (heatsink_halt)

Orchestration via unified field Φ_orchestrate:
Φ_team(team, task) = Σᵢ Φᵢ(agentᵢ) + Σᵢ<ⱼ Φ_coordination(agentᵢ, agentⱼ)

Where coordination field captures:
- Dependency: Agent j needs output from agent i
- Conflict: Agents compete for resources
- Synergy: Agents collaborate on shared goals

Triumvirate Integration:
Swarm bug detection maps to Triumvirate roles via severity-based logic:
- Severity ≥ 85 + incomplete proof → WardenAgent (proof validation)
- Severity ≥ 85 + other → JudgeAgent (critical issues)
- Warnings → JudgeAgent (hold state for assessment)
- Other → BuilderAgent (forward progress)

Hardware Mapping:
- BuilderAgent → manifold_reg (Topological State, ADD clock)
- WardenAgent → stark_trace & warden_valid (Integrity, SUBTRACT clock)
- JudgeAgent → heatsink_halt (Energy Guard, PAUSE clock)

Per AGENTS.md §1.4: Q16_16 fixed-point for hardware extraction.
Per AGENTS.md §2: PascalCase types, camelCase functions.
Per AGENTS.md §4: Every def has eval witness or theorem.

NII-02 TRANSLATION ENGINE ASSIGNMENT:
====================================
This file is assigned to NII-02 Translation Engine for:
- Translation of agent orchestration field to hardware-accelerated computation
- Extraction of coordination patterns for multi-agent hardware scheduling
- Translation of task dependency graphs to hardware resource allocation
- Formalization of agent field dynamics for hardware implementation

Translation responsibilities:
1. Map AgentFieldParams and CoordinationParams to hardware-native representation
2. Translate orchestration field computation to GPU/accelerator kernels
3. Extract task scheduling algorithms for hardware dispatch
4. Formalize agent state transitions for hardware state machines

TODO(lean-port): Coordinate with SubagentOrchestrator.lean
TODO(lean-port): Define agent communication protocols
TODO(lean-port): Prove orchestration stability (no deadlock)
-/

import AgenticHardware
import AgenticCore
import AgenticOrchestrationField
import AgenticTaskAssignment
import AgenticTheorems

-- Re-export all components for backward compatibility
open Semantics.AgenticOrchestration

/-! ## Layered Orchestration

```
┌─────────────────────────────────────────────────────────────┐
│  LAYER 3: AgenticOrchestration                              │
│  ├── Research pipeline: search → extract → formalize       │
│  ├── Agent teams: specialized workers                        │
│  └── Task graph: dependency management                       │
├─────────────────────────────────────────────────────────────┤
│  LAYER 2: SubagentOrchestrator                               │
│  ├── Domain coordination: compression ↔ field-physics        │
│  ├── Resource allocation: CPU, memory, SRAM                │
│  └── Convergence: multi-domain theorem proving              │
├─────────────────────────────────────────────────────────────┤
│  LAYER 1: Individual Agents                                  │
│  ├── SearchAgent → ScholarOrchestrator (Python)              │
│  ├── FormalizeAgent → GenomicCompression.lean                │
│  └── ValidateAgent → unified_field_validation.py             │
└─────────────────────────────────────────────────────────────┘
```

## Communication Protocol

Agents communicate via:
1. **Message passing**: Async queue (Kafka/RabbitMQ style)
2. **Shared state**: OTOM knowledge graph
3. **Direct RPC**: For synchronous coordination

Message types:
- `TaskRequest`: Assign new task
- `TaskComplete`: Report results
- `DependencyMet`: Notify unblocking
- `ResourceRequest`: Ask for allocation
-/ 

-- ═══════════════════════════════════════════════════════════════════════════
-- Verification Examples
-- ═══════════════════════════════════════════════════════════════════════════

#eval let agents := [
  { id := "A1", agentType := AgentType.searchAgent, currentTask := none,
    completedTasks := [], outputBuffer := [], load := zero, status := AgentStatus.idle },
  { id := "A2", agentType := AgentType.extractAgent, currentTask := none,
    completedTasks := [], outputBuffer := [], load := zero, status := AgentStatus.idle }
]
let tasks := researchPipeline.take 2
let params := { rhoCapability := one, vEfficiency := one, tauLoad := zero, qReliability := one }
let (updated, completed, steps) := runOrchestration agents tasks params 
  { dependencyStrength := ofNat 32768, conflictPenalty := ofNat 6553, synergyBonus := ofNat 19660 }
steps
-- Expected: ~20 steps (simulated)

#eval assignTask researchPipeline[0] [
  { id := "A1", agentType := AgentType.searchAgent, currentTask := none,
    completedTasks := [], outputBuffer := [], load := zero, status := AgentStatus.idle },
  { id := "A2", agentType := AgentType.extractAgent, currentTask := none,
    completedTasks := [], outputBuffer := [], load := zero, status := AgentStatus.idle }
] { rhoCapability := one, vEfficiency := one, tauLoad := zero, qReliability := one }
-- Expected: A1 (searchAgent for search task)

/-! ## Roadmap

### Immediate (This Week)
- [ ] Connect to SubagentOrchestrator.lean
- [ ] Define agent communication protocol (Lean + Python)
- [ ] Implement Python AgentShim classes

### Short-term (Next 2 Weeks)
- [ ] Full research pipeline: 7 tasks, 5 agents
- [ ] Integration with GenomicCompression + ResearchAgent
- [ ] Demo: Autonomous paper analysis end-to-end

### Medium-term (Next Month)
- [ ] Multi-team orchestration (multiple research projects)
- [ ] Dynamic agent spawning based on workload
- [ ] Paper: "Agentic Orchestration for Scientific Discovery"

## Open Questions

1. **Deadlock prevention**: How to guarantee no circular dependencies?
2. **Fault tolerance**: Agent failure recovery mechanisms?
3. **Scalability**: 10 agents? 100 agents? 1000 agents?
4. **Human-in-the-loop**: When should human review be required?
-/ 

-- TODO(lean-port):
-- 1. Complete all proof placeholders in theorems
-- 2. Connect to SubagentOrchestrator domain definitions
-- 3. Define agent communication protocol (async message passing)
-- 4. Prove orchestration stability (no deadlock, no starvation)
-- 5. Implement Python AgentShim for each agent type
-- 6. Extract coordination patterns from InternAgent-1.5 paper

end Semantics.AgenticOrchestration