# AITBC + OpenClaw Integration Implementation Plan

## Executive Summary

This plan outlines the comprehensive integration between AITBC (Autonomous Intelligent Trading Blockchain Computing) and OpenClaw, a modern AI agent orchestration framework. The integration enables OpenClaw agents to seamlessly leverage AITBC's decentralized GPU network for heavy computational tasks while maintaining local execution capabilities for lightweight operations.

### Key Integration Points
- **OpenClaw Ollama Provider**: Direct integration with AITBC coordinator endpoint using ZK-attested miners
- **Agent Skills Routing**: Intelligent job offloading via AITBC `/job` API with AIT token micropayments
- **Marketplace Integration**: One-click deployment of marketplace models to OpenClaw environments
- **Edge Miner Client**: Optional OpenClaw daemon for personal always-on AI agents
- **Hybrid Architecture**: Local execution fallback with AITBC offload for large models (>8GB)

## Current Infrastructure Analysis

### AITBC Components
Based on the current codebase, AITBC provides:

**Coordinator API** (`/apps/coordinator-api/`):
- Job submission and management via `/job` endpoints
- GPU marketplace with miner registration and bidding
- ZK proof verification for job attestation
- Token-based micropayment system

**GPU Mining Infrastructure**:
- Host-based miners with Ollama integration
- Real-time GPU capability detection
- Decentralized job execution with proof-of-work

**Model Marketplace** (`/apps/marketplace/`):
- On-chain model trading with NFT wrappers
- Quality scanning and malware detection
- Auto-deployment to GPU inference jobs

### OpenClaw Framework Assumptions
OpenClaw is assumed to be an AI agent orchestration platform with:
- Ollama-compatible inference providers
- Agent skill routing and orchestration
- Local model execution capabilities
- Plugin architecture for external integrations

## Implementation Architecture

### Hybrid Execution Model
```python
class HybridExecutionEngine:
    """Hybrid local-AITBC execution engine for OpenClaw"""

    def __init__(
        self,
        local_ollama: OllamaClient,
        aitbc_client: AITBCClient,
        model_router: ModelRouter
    ):
        self.local = local_ollama
        self.aitbc = aitbc_client
        self.router = model_router
        self.execution_thresholds = {
            "max_local_model_size": 8 * 1024 * 1024 * 1024,  # 8GB
            "local_inference_timeout": 300,  # 5 minutes
            "cost_efficiency_threshold": 0.8  # 80% cost efficiency
        }

    async def execute_agent_task(
        self,
        task_spec: AgentTask,
        execution_context: ExecutionContext
    ) -> TaskResult:
        """Execute agent task with hybrid local/AITBC routing"""

        # Determine optimal execution strategy
        execution_plan = await self._plan_execution(task_spec, execution_context)

        if execution_plan.strategy == "local":
            return await self._execute_local(task_spec, execution_context)
        elif execution_plan.strategy == "aitbc":
            return await self._execute_aitbc(task_spec, execution_context)
        elif execution_plan.strategy == "hybrid":
            return await self._execute_hybrid(task_spec, execution_context)

        raise ExecutionStrategyError(f"Unknown strategy: {execution_plan.strategy}")

    async def _plan_execution(
        self,
        task: AgentTask,
        context: ExecutionContext
    ) -> ExecutionPlan:
        """Plan optimal execution strategy"""

        # Check model requirements
        model_size = await self._estimate_model_size(task.model_requirements)
        compute_complexity = self._assess_compute_complexity(task)

        # Local execution criteria
        can_execute_local = (
            model_size <= self.execution_thresholds["max_local_model_size"] and
            self.local.has_model_available(task.model_requirements) and
            context.allow_local_execution
        )

        # AITBC execution criteria
        should_use_aitbc = (
            not can_execute_local or
            compute_complexity > 0.7 or  # High complexity tasks
            context.force_aitbc_execution or
            await self._is_aitbc_cost_effective(task, context)
        )

        if can_execute_local and not should_use_aitbc:
            return ExecutionPlan(strategy="local", reason="optimal_local")
        elif should_use_aitbc:
            return ExecutionPlan(strategy="aitbc", reason="compute_intensive")
        else:
            return ExecutionPlan(strategy="hybrid", reason="balanced_approach")

    async def _execute_hybrid(
        self,
        task: AgentTask,
        context: ExecutionContext
    ) -> TaskResult:
        """Execute with hybrid local/AITBC approach"""

        # Start local execution
        local_task = asyncio.create_task(
            self._execute_local(task, context)
        )

        # Prepare AITBC backup
        aitbc_task = asyncio.create_task(
            self._prepare_aitbc_backup(task, context)
        )

        # Race conditions with timeout
        done, pending = await asyncio.wait(
            [local_task, aitbc_task],
            timeout=self.execution_thresholds["local_inference_timeout"],
            return_when=asyncio.FIRST_COMPLETED
        )

        # Cancel pending tasks
        for task in pending:
            task.cancel()

        # Return first completed result
        if done:
            return await done[0]

        # Fallback to AITBC if local times out
        return await self._execute_aitbc(task, context)
```

### OpenClaw Provider Implementation

#### AITBC Ollama Provider
```python
class AITBCOllamaProvider:
    """OpenClaw-compatible Ollama provider using AITBC network"""

    def __init__(
        self,
        aitbc_coordinator_url: str,
        api_key: str,
        zk_verification: bool = True
    ):
        self.aitbc_client = AITBCClient(
            coordinator_url=aitbc_coordinator_url,
            api_key=api_key
        )
        self.zk_enabled = zk_verification
        self.active_jobs = {}  # job_id -> JobHandle

    async def list_models(self) -> List[ModelInfo]:
        """List available models on AITBC network"""

        # Query available GPU miners and their models
        gpu_inventory = await self.aitbc_client.get_gpu_inventory()

        models = []
        for gpu in gpu_inventory:
            for model in gpu.available_models:
                # Create Ollama-compatible model info
                model_info = ModelInfo(
                    name=f"{model.name}@{gpu.miner_id}",
                    size=model.size_bytes,
                    modified_at=gpu.last_seen,
                    digest=model.hash,
                    details={
                        "format": model.format,
                        "family": model.family,
                        "families": model.families,
                        "parameter_size": model.parameter_count,
                        "quantization_level": model.quantization,
                        "gpu_accelerated": True,
                        "zk_attested": gpu.zk_attested,
                        "region": gpu.region,
                        "price_per_token": gpu.price_per_token
                    }
                )
                models.append(model_info)

        return models

    async def generate(
        self,
        model: str,
        prompt: str,
        options: dict = None
    ) -> GenerationResponse:
        """Generate text using AITBC network"""

        # Parse model specification (model@miner_id)
        model_name, miner_id = self._parse_model_spec(model)

        # Create job specification
        job_spec = JobSpec(
            type="ollama_inference",
            model=model_name,
            prompt=prompt,
            options=options or {},
            miner_preferences={
                "preferred_miner": miner_id,
                "zk_required": self.zk_enabled,
                "max_cost": options.get("max_cost", 0.01)  # Default 1 cent
            }
        )

        # Submit job to AITBC
        job_result = await self.aitbc_client.submit_job(job_spec)

        # Convert to Ollama-compatible response
        return GenerationResponse(
            model=model,
            created_at=job_result.completed_at,
            response=job_result.output,
            done=True,
            context=job_result.context_tokens,
            total_duration=job_result.total_duration,
            load_duration=job_result.load_duration,
            prompt_eval_count=job_result.prompt_tokens,
            prompt_eval_duration=job_result.prompt_eval_duration,
            eval_count=job_result.output_tokens,
            eval_duration=job_result.eval_duration,
            aitbc_metadata={
                "job_id": job_result.job_id,
                "miner_id": job_result.miner_id,
                "cost_ait": job_result.cost_ait,
                "zk_proof": job_result.zk_proof if self.zk_enabled else None
            }
        )

    async def generate_with_stream(
        self,
        model: str,
        prompt: str,
        options: dict = None
    ) -> AsyncGenerator[GenerationChunk, None]:
        """Streaming generation with AITBC"""

        # Parse model spec
        model_name, miner_id = self._parse_model_spec(model)

        # Create streaming job
        job_spec = JobSpec(
            type="ollama_inference_stream",
            model=model_name,
            prompt=prompt,
            options={**(options or {}), "stream": True},
            miner_preferences={
                "preferred_miner": miner_id,
                "zk_required": self.zk_enabled
            }
        )

        # Submit streaming job
        async for chunk in self.aitbc_client.submit_streaming_job(job_spec):
            yield GenerationChunk(
                model=model,
                created_at=chunk.timestamp,
                response=chunk.text,
                done=chunk.done,
                aitbc_metadata={
                    "chunk_id": chunk.chunk_id,
                    "job_id": chunk.job_id
                }
            )

    def _parse_model_spec(self, model_spec: str) -> Tuple[str, Optional[str]]:
        """Parse model@miner_id specification"""
        if "@" in model_spec:
            model_name, miner_id = model_spec.split("@", 1)
            return model_name, miner_id
        return model_spec, None
```

### Agent Skills Routing System

#### Micropayment-Enabled Skill Router
```python
class AgentSkillRouter:
    """Routes agent skills with AITBC offloading and micropayments"""

    def __init__(
        self,
        skill_registry: SkillRegistry,
        aitbc_client: AITBCClient,
        token_wallet: AITTokenWallet
    ):
        self.skills = skill_registry
        self.aitbc = aitbc_client
        self.wallet = token_wallet
        self.skill_cost_cache = {}  # Cache skill execution costs

    async def execute_skill(
        self,
        skill_name: str,
        parameters: dict,
        execution_context: dict = None
    ) -> SkillResult:
        """Execute skill with intelligent routing"""

        skill = await self.skills.get_skill(skill_name)
        if not skill:
            raise SkillNotFoundError(f"Skill {skill_name} not found")

        # Assess execution requirements
        requirements = await self._assess_skill_requirements(skill, parameters)

        # Determine execution strategy
        strategy = await self._determine_execution_strategy(
            skill, requirements, execution_context
        )

        if strategy == "local":
            return await self._execute_skill_local(skill, parameters)
        elif strategy == "aitbc":
            return await self._execute_skill_aitbc(skill, parameters, requirements)
        elif strategy == "hybrid":
            return await self._execute_skill_hybrid(skill, parameters, requirements)

    async def _determine_execution_strategy(
        self,
        skill: Skill,
        requirements: SkillRequirements,
        context: dict
    ) -> str:
        """Determine optimal execution strategy"""

        # Check computational requirements
        if requirements.compute_intensity > 0.8:  # Very compute intensive
            return "aitbc"
        elif requirements.model_size > 4 * 1024 * 1024 * 1024:  # >4GB models
            return "aitbc"
        elif requirements.expected_duration > 120:  # >2 minutes
            return "aitbc"

        # Check cost effectiveness
        aitbc_cost = await self._estimate_aitbc_cost(skill, requirements)
        local_cost = await self._estimate_local_cost(skill, requirements)

        if aitbc_cost < local_cost * 0.8:  # AITBC 20% cheaper
            return "aitbc"

        # Check local availability
        if await self._is_skill_available_locally(skill):
            return "local"

        # Default to hybrid approach
        return "hybrid"

    async def _execute_skill_aitbc(
        self,
        skill: Skill,
        parameters: dict,
        requirements: SkillRequirements
    ) -> SkillResult:
        """Execute skill on AITBC network with micropayments"""

        # Prepare job specification
        job_spec = JobSpec(
            type="skill_execution",
            skill_name=skill.name,
            parameters=parameters,
            requirements=requirements,
            payment={
                "wallet_address": self.wallet.address,
                "max_cost_ait": requirements.max_cost_ait,
                "auto_approve": True
            }
        )

        # Submit job
        job_result = await self.aitbc.submit_job(job_spec)

        # Verify and record payment
        if job_result.cost_ait > 0:
            await self._record_micropayment(
                job_result.job_id,
                job_result.cost_ait,
                job_result.miner_address
            )

        return SkillResult(
            skill_name=skill.name,
            result=job_result.output,
            execution_time=job_result.total_duration,
            cost_ait=job_result.cost_ait,
            execution_provider="aitbc",
            metadata={
                "job_id": job_result.job_id,
                "miner_id": job_result.miner_id,
                "zk_proof": job_result.zk_proof
            }
        )

    async def _estimate_aitbc_cost(
        self,
        skill: Skill,
        requirements: SkillRequirements
    ) -> float:
        """Estimate AITBC execution cost"""

        # Get current market rates
        market_rates = await self.aitbc.get_market_rates()

        # Calculate based on compute requirements
        base_cost = market_rates.base_inference_cost
        compute_multiplier = requirements.compute_intensity
        duration_multiplier = min(requirements.expected_duration / 60, 10)  # Cap at 10 minutes

        estimated_cost = base_cost * compute_multiplier * duration_multiplier

        # Cache for future use
        cache_key = f"{skill.name}_{hash(str(requirements))}"
        self.skill_cost_cache[cache_key] = {
            "cost": estimated_cost,
            "timestamp": datetime.utcnow(),
            "valid_for": timedelta(minutes=5)
        }

        return estimated_cost

    async def _record_micropayment(
        self,
        job_id: str,
        amount_ait: float,
        miner_address: str
    ):
        """Record micropayment transaction"""

        transaction = MicropaymentTransaction(
            job_id=job_id,
            amount_ait=amount_ait,
            from_address=self.wallet.address,
            to_address=miner_address,
            timestamp=datetime.utcnow(),
            transaction_type="skill_execution",
            metadata={
                "aitbc_job_id": job_id,
                "execution_type": "skill_routing"
            }
        )

        await self.wallet.record_transaction(transaction)

        # Update cost cache
        await self._update_cost_cache(job_id, amount_ait)
```

### Model Marketplace Integration

#### One-Click OpenClaw Deployment
```python
class OpenClawMarketplaceIntegration:
    """Integrate AITBC marketplace with OpenClaw deployment"""

    def __init__(
        self,
        marketplace_client: MarketplaceClient,
        openclaw_client: OpenClawClient,
        deployment_service: DeploymentService
    ):
        self.marketplace = marketplace_client
        self.openclaw = openclaw_client
        self.deployment = deployment_service

    async def deploy_to_openclaw(
        self,
        model_id: str,
        openclaw_environment: str,
        deployment_config: dict = None
    ) -> DeploymentResult:
        """One-click deployment from marketplace to OpenClaw"""

        # Verify model license
        license_check = await self.marketplace.verify_license(model_id)
        if not license_check.valid:
            raise LicenseError("Model license verification failed")

        # Download model
        model_data = await self.marketplace.download_model(model_id)

        # Prepare OpenClaw deployment
        deployment_spec = await self._prepare_openclaw_deployment(
            model_data, openclaw_environment, deployment_config
        )

        # Deploy to OpenClaw
        deployment_result = await self.openclaw.deploy_model(deployment_spec)

        # Register with AITBC marketplace
        await self.marketplace.register_deployment(
            model_id=model_id,
            deployment_id=deployment_result.deployment_id,
            platform="openclaw",
            environment=openclaw_environment
        )

        return DeploymentResult(
            success=True,
            model_id=model_id,
            deployment_id=deployment_result.deployment_id,
            platform="openclaw",
            endpoint=deployment_result.endpoint,
            metadata={
                "environment": openclaw_environment,
                "aitbc_model_id": model_id,
                "deployment_config": deployment_config
            }
        )

    async def _prepare_openclaw_deployment(
        self,
        model_data: dict,
        environment: str,
        config: dict = None
    ) -> OpenClawDeploymentSpec:
        """Prepare model for OpenClaw deployment"""

        # Determine optimal configuration
        optimal_config = await self._optimize_for_openclaw(
            model_data, environment, config
        )

        # Create deployment specification
        spec = OpenClawDeploymentSpec(
            model_name=model_data["name"],
            model_data=model_data["data"],
            model_format=model_data["format"],
            quantization=optimal_config["quantization"],
            tensor_parallel_size=optimal_config["tensor_parallel"],
            gpu_memory_limit=optimal_config["gpu_memory_limit"],
            max_concurrent_requests=optimal_config["max_concurrent"],
            environment_overrides=optimal_config["environment_vars"],
            monitoring_enabled=True,
            aitbc_integration={
                "enabled": True,
                "fallback_threshold": 0.8,  # 80% utilization triggers fallback
                "cost_monitoring": True
            }
        )

        return spec

    async def get_deployment_status(
        self,
        deployment_id: str
    ) -> DeploymentStatus:
        """Get deployment status from OpenClaw"""

        # Query OpenClaw
        status = await self.openclaw.get_deployment_status(deployment_id)

        # Enhance with AITBC metrics
        aitbc_metrics = await self._get_aitbc_metrics(deployment_id)

        return DeploymentStatus(
            deployment_id=deployment_id,
            status=status.status,
            health=status.health,
            utilization=status.utilization,
            aitbc_fallbacks=aitbc_metrics.fallback_count,
            total_requests=status.total_requests,
            error_rate=status.error_rate,
            average_latency=status.average_latency,
            cost_efficiency=aitbc_metrics.cost_efficiency
        )
```

### Edge Miner Client with OpenClaw Daemon

#### Personal Agent Daemon
```python
class OpenClawDaemon:
    """Optional OpenClaw daemon for edge miners"""

    def __init__(
        self,
        aitbc_miner: AITBCMiner,
        openclaw_engine: OpenClawEngine,
        agent_registry: AgentRegistry
    ):
        self.miner = aitbc_miner
        self.openclaw = openclaw_engine
        self.agents = agent_registry
        self.daemon_config = {
            "auto_start_agents": True,
            "max_concurrent_agents": 3,
            "resource_limits": {
                "cpu_percent": 80,
                "memory_percent": 70,
                "gpu_memory_percent": 60
            }
        }

    async def start_daemon(self):
        """Start the OpenClaw daemon service"""

        logger.info("Starting OpenClaw daemon for AITBC miner")

        # Register daemon capabilities
        await self._register_daemon_capabilities()

        # Start agent monitoring
        agent_monitor_task = asyncio.create_task(self._monitor_agents())

        # Start resource management
        resource_manager_task = asyncio.create_task(self._manage_resources())

        # Start integration service
        integration_task = asyncio.create_task(self._handle_integrations())

        # Wait for all services
        await asyncio.gather(
            agent_monitor_task,
            resource_manager_task,
            integration_task
        )

    async def register_personal_agent(
        self,
        agent_spec: AgentSpec,
        capabilities: dict
    ) -> AgentRegistration:
        """Register a personal always-on agent"""

        # Validate agent specification
        validation = await self._validate_agent_spec(agent_spec)
        if not validation.valid:
            raise AgentValidationError(validation.errors)

        # Check resource availability
        resource_check = await self._check_resource_availability(capabilities)
        if not resource_check.available:
            raise ResourceUnavailableError(resource_check.reason)

        # Register with OpenClaw
        registration = await self.openclaw.register_agent(agent_spec)

        # Enhance with AITBC capabilities
        enhanced_registration = await self._enhance_with_aitbc_capabilities(
            registration, capabilities
        )

        # Store registration
        await self.agents.store_registration(enhanced_registration)

        # Start agent if auto-start enabled
        if self.daemon_config["auto_start_agents"]:
            await self._start_agent(enhanced_registration.agent_id)

        return enhanced_registration

    async def _monitor_agents(self):
        """Monitor registered agents and their resource usage"""

        while True:
            try:
                # Get all active agents
                active_agents = await self.agents.get_active_agents()

                for agent in active_agents:
                    # Check agent health
                    health = await self._check_agent_health(agent.agent_id)

                    if health.status != "healthy":
                        logger.warning(f"Agent {agent.agent_id} health: {health.status}")
                        await self._handle_unhealthy_agent(agent, health)

                    # Monitor resource usage
                    usage = await self._monitor_agent_resources(agent.agent_id)

                    # Enforce resource limits
                    if usage.cpu_percent > self.daemon_config["resource_limits"]["cpu_percent"]:
                        await self._throttle_agent(agent.agent_id, "cpu_limit")

                    if usage.memory_percent > self.daemon_config["resource_limits"]["memory_percent"]:
                        await self._throttle_agent(agent.agent_id, "memory_limit")

                # Check for agent scheduling opportunities
                await self._schedule_agents_if_needed()

            except Exception as e:
                logger.error(f"Agent monitoring error: {e}")

            await asyncio.sleep(30)  # Monitor every 30 seconds

    async def _handle_integrations(self):
        """Handle integrations between OpenClaw and AITBC"""

        while True:
            try:
                # Check for AITBC jobs that could benefit from local agents
                pending_jobs = await self.miner.get_pending_jobs()

                for job in pending_jobs:
                    # Check if local agent can handle this job
                    capable_agents = await self._find_capable_agents(job)

                    if capable_agents:
                        # Route job to local agent
                        await self._route_job_to_agent(job, capable_agents[0])

                # Check for agent tasks that need AITBC offload
                agent_tasks = await self._get_pending_agent_tasks()

                for task in agent_tasks:
                    if await self._should_offload_to_aitbc(task):
                        await self._offload_task_to_aitbc(task)

            except Exception as e:
                logger.error(f"Integration handling error: {e}")

            await asyncio.sleep(10)  # Check every 10 seconds

    async def _route_job_to_agent(
        self,
        aitbc_job: AITBCJob,
        agent: RegisteredAgent
    ):
        """Route AITBC job to local OpenClaw agent"""

        # Convert AITBC job to OpenClaw task
        task_spec = await self._convert_aitbc_job_to_task(aitbc_job)

        # Submit to agent
        task_result = await self.openclaw.submit_task_to_agent(
            agent_id=agent.agent_id,
            task_spec=task_spec
        )

        # Report completion back to AITBC
        await self.miner.report_job_completion(
            job_id=aitbc_job.job_id,
            result=task_result.result,
            proof=task_result.proof
        )

    async def _offload_task_to_aitbc(
        self,
        agent_task: AgentTask
    ):
        """Offload agent task to AITBC network"""

        # Convert to AITBC job
        aitbc_job_spec = await self._convert_agent_task_to_aitbc_job(agent_task)

        # Submit to AITBC
        job_result = await self.miner.submit_job_to_network(aitbc_job_spec)

        # Return result to agent
        await self.openclaw.return_task_result(
            task_id=agent_task.task_id,
            result=job_result.output,
            metadata={
                "aitbc_job_id": job_result.job_id,
                "execution_cost": job_result.cost_ait
            }
        )
```

### API Integration Layer

#### REST API Extensions
```python
# OpenClaw integration endpoints for AITBC coordinator

@app.post("/api/v1/openclaw/models/deploy")
async def deploy_model_to_openclaw(
    request: DeployModelRequest,
    current_user: User = Depends(get_current_user)
):
    """Deploy marketplace model to OpenClaw environment"""

    integration = OpenClawMarketplaceIntegration(
        marketplace_client=get_marketplace_client(),
        openclaw_client=get_openclaw_client(),
        deployment_service=get_deployment_service()
    )

    result = await integration.deploy_to_openclaw(
        model_id=request.model_id,
        openclaw_environment=request.environment,
        deployment_config=request.config
    )

    return APIResponse(
        success=True,
        data=result,
        message="Model deployed to OpenClaw successfully"
    )

@app.post("/api/v1/openclaw/agents/register")
async def register_openclaw_agent(
    request: RegisterAgentRequest,
    current_user: User = Depends(get_current_user)
):
    """Register OpenClaw agent with AITBC miner"""

    daemon = get_openclaw_daemon()

    registration = await daemon.register_personal_agent(
        agent_spec=request.agent_spec,
        capabilities=request.capabilities
    )

    return APIResponse(
        success=True,
        data=registration,
        message="OpenClaw agent registered successfully"
    )

@app.post("/api/v1/openclaw/jobs/route")
async def route_job_via_openclaw(
    request: RouteJobRequest,
    current_user: User = Depends(get_current_user)
):
    """Route job through OpenClaw skill system"""

    router = get_skill_router()

    result = await router.execute_skill(
        skill_name=request.skill_name,
        parameters=request.parameters,
        execution_context=request.context
    )

    return APIResponse(
        success=True,
        data=result,
        message="Job routed through OpenClaw successfully"
    )

@app.get("/api/v1/openclaw/status")
async def get_openclaw_integration_status():
    """Get OpenClaw integration status"""

    status = await get_openclaw_integration_status()

    return APIResponse(
        success=True,
        data=status,
        message="OpenClaw integration status retrieved"
    )


## Additional OpenClaw Integration Gaps & Solutions

### ZK-Proof Chaining for Hybrid Fallback

#### Chained Proof Verification
```python
class ZKProofChainManager:
    """ZK proof chaining for hybrid execution verification"""

    def __init__(
        self,
        zk_service: ZKProofService,
        proof_registry: ProofRegistry,
        chain_validator: ChainValidator
    ):
        self.zk = zk_service
        self.registry = proof_registry
        self.validator = chain_validator

    async def create_hybrid_execution_chain(
        self,
        local_execution: LocalExecution,
        aitbc_fallback: AITBCExecution,
        chain_metadata: dict
    ) -> ProofChain:
        """Create ZK proof chain for hybrid execution"""

        # Generate local execution proof
        local_proof = await self._generate_local_execution_proof(local_execution)

        # Generate AITBC fallback proof
        aitbc_proof = await self._generate_aitbc_execution_proof(aitbc_fallback)

        # Create proof linkage
        chain_link = await self._create_proof_linkage(
            local_proof, aitbc_proof, chain_metadata
        )

        # Generate chain verification proof
        chain_proof = await self._generate_chain_verification_proof(
            local_proof, aitbc_proof, chain_link
        )

        # Register complete chain
        chain_id = await self.registry.register_proof_chain(
            ProofChain(
                chain_id=uuid4().hex,
                local_proof=local_proof,
                aitbc_proof=aitbc_proof,
                chain_link=chain_link,
                chain_proof=chain_proof,
                metadata={
                    **chain_metadata,
                    "chain_type": "hybrid_fallback",
                    "created_at": datetime.utcnow().isoformat()
                }
            )
        )

        return chain_id

    async def verify_proof_chain(
        self,
        chain_id: str
    ) -> ChainVerification:
        """Verify complete proof chain"""

        chain = await self.registry.get_proof_chain(chain_id)

        # Verify individual proofs
        local_valid = await self.zk.verify_proof(chain.local_proof)
        aitbc_valid = await self.zk.verify_proof(chain.aitbc_proof)
        chain_valid = await self.zk.verify_proof(chain.chain_proof)

        # Verify linkage integrity
        linkage_valid = await self._verify_linkage_integrity(chain.chain_link)

        return ChainVerification(
            chain_id=chain_id,
            local_proof_valid=local_valid,
            aitbc_proof_valid=aitbc_valid,
            chain_proof_valid=chain_valid,
            linkage_valid=linkage_valid,
            overall_valid=all([local_valid, aitbc_valid, chain_valid, linkage_valid])
        )
```

### OpenClaw Version Pinning + Upgrade Path

#### Version Management System
```python
class OpenClawVersionManager:
    """Version pinning and upgrade management for OpenClaw"""

    def __init__(
        self,
        version_registry: VersionRegistry,
        compatibility_checker: CompatibilityChecker,
        upgrade_orchestrator: UpgradeOrchestrator
    ):
        self.versions = version_registry
        self.compatibility = compatibility_checker
        self.upgrades = upgrade_orchestrator
        self.version_pins = {}  # component -> pinned_version

    async def pin_openclaw_version(
        self,
        component: str,
        version_spec: str,
        pin_reason: str
    ) -> VersionPin:
        """Pin OpenClaw component to specific version"""

        # Validate version specification
        validation = await self._validate_version_spec(component, version_spec)
        if not validation.valid:
            raise InvalidVersionSpecError(validation.error_message)

        # Check compatibility
        compatibility = await self.compatibility.check_version_compatibility(
            component, version_spec
        )

        if not compatibility.compatible:
            raise IncompatibleVersionError(
                f"Version {version_spec} incompatible: {compatibility.issues}"
            )

        # Create version pin
        version_pin = VersionPin(
            component=component,
            version_spec=version_spec,
            pin_reason=pin_reason,
            pinned_at=datetime.utcnow(),
            compatibility_status=compatibility,
            security_audit=await self._perform_security_audit(version_spec)
        )

        # Store pin
        await self.versions.store_version_pin(version_pin)
        self.version_pins[component] = version_pin

        return version_pin

    async def check_for_updates(
        self,
        component: str,
        include_prerelease: bool = False
    ) -> UpdateCheck:
        """Check for available updates for pinned component"""

        current_pin = self.version_pins.get(component)
        if not current_pin:
            raise ComponentNotPinnedError(f"Component {component} not pinned")

        # Get available versions
        available_versions = await self.versions.get_available_versions(
            component, include_prerelease
        )

        # Filter versions newer than current pin
        current_version = self._parse_version(current_pin.version_spec)
        newer_versions = [
            v for v in available_versions
            if self._parse_version(v.version) > current_version
        ]

        if not newer_versions:
            return UpdateCheck(
                component=component,
                current_version=current_pin.version_spec,
                updates_available=False
            )

        return UpdateCheck(
            component=component,
            current_version=current_pin.version_spec,
            updates_available=True,
            latest_version=newer_versions[0].version,
            available_updates=newer_versions
        )

    async def execute_upgrade(
        self,
        component: str,
        target_version: str,
        dry_run: bool = False
    ) -> UpgradeExecution:
        """Execute upgrade according to plan"""

        current_pin = self.version_pins.get(component)
        if not current_pin:
            raise ComponentNotPinnedError(f"Component {component} not pinned")

        # Generate upgrade plan
        upgrade_steps = await self._generate_upgrade_steps(
            component, current_pin.version_spec, target_version
        )

        execution = UpgradeExecution(
            component=component,
            target_version=target_version,
            started_at=datetime.utcnow(),
            dry_run=dry_run
        )

        try:
            for step in upgrade_steps:
                step_result = await self._execute_upgrade_step(step, dry_run)
                execution.steps_executed.append(step_result)

                if not step_result.success:
                    execution.success = False
                    execution.failed_at_step = step.step_id
                    break

            else:
                execution.success = True
                execution.completed_at = datetime.utcnow()

                if not dry_run:
                    await self._update_version_pin(component, target_version)

        except Exception as e:
            execution.success = False
            execution.error_message = str(e)

        return execution

    def _parse_version(self, version_spec: str) -> tuple:
        """Parse version string into comparable tuple"""
        parts = version_spec.split('.')
        return tuple(int(x) for x in parts[:3])
```

### Phased Implementation
1. **Phase 1: Provider Integration** - Implement AITBC Ollama provider for OpenClaw
2. **Phase 2: Skill Routing** - Add intelligent skill offloading with micropayments

### Infrastructure Requirements
- NFT license contract deployment
- FHE computation infrastructure
- ZK proof generation services
- Offline data synchronization
- Comprehensive metrics collection
- Version management system

## Risk Assessment

### Regulatory Risks
- **EU AI Act Non-Compliance**: High fines for non-compliant AI systems
- **Data Protection Violations**: GDPR breaches from improper data handling
- **License Enforcement Failure**: Revenue loss from unauthorized usage

### Technical Risks
- **ZK Proof Overhead**: Performance impact from cryptographic operations
- **FHE Computation Cost**: High computational requirements for encrypted inference
- **Offline Synchronization Conflicts**: Data consistency issues during offline operation

## Success Metrics

### Compliance Targets
- 100% of agents with valid EU AI Act assessments
- 95% successful license verification rate
- Zero regulatory violations in production

### Performance Targets
- <5% performance degradation from security features
- 99.9% offline sync success rate
- <10 second average agent discovery time

### Business Impact
- Expanded enterprise adoption through regulatory compliance
- New licensing revenue streams from NFT marketplace
- Enhanced agent ecosystem through marketplace discovery

## Timeline

### Month 1-2: Compliance & Security
- EU AI Act compliance framework implementation
- NFT license enforcement system
- FHE prompt support development

### Month 3-4: Agent Infrastructure
- Wallet sandboxing and spending controls
- Agent marketplace discovery
- Slashing mechanism for task failures

### Month 5-6: Resilience & Monitoring
- Offline synchronization system
- Comprehensive metrics collection
- Version management and upgrade paths

### Month 7-8: Production Deployment
- End-to-end testing and validation
- Regulatory compliance audits
- Production optimization and scaling

## Resource Requirements

### Development Team
- 3 Backend Engineers (Python/Solidity)
- 2 Security Engineers (cryptography/compliance)
- 1 DevOps Engineer (infrastructure/monitoring)
- 1 Legal/Compliance Specialist (EU AI Act)
- 2 QA Engineers (testing/validation)

### Infrastructure Costs
- ZK proof generation infrastructure
- FHE computation resources
- NFT contract deployment and maintenance
- Compliance monitoring systems
- Offline data storage and synchronization
